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A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM (2403.07012v2)
Published 9 Mar 2024 in cs.LG
Abstract: With the growing demand for energy and increased environmental awareness, Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. By analyzing total power load data, NILM infers the energy usage of individual appliances without the need for separate sensors, enabling real-time monitoring from a few locations. This approach helps users understand consumption patterns, enhance energy efficiency, and detect anomalies for effective energy management. However, NILM datasets often suffer from issues such as sensor failures and data loss, compromising data integrity, thereby impacting subsequent analysis and applications. Traditional imputation methods, such as linear interpolation and matrix factorization, struggle with nonlinear relationships and are sensitive to sparse data, resulting in information loss. To address these challenges, this paper proposes a Proportional-Integral-Derivative (PID) Controlled Non-Negative Latent Factorization of Tensor (PNLF) model, which dynamically adjusts parameter gradients to improve convergence, stability, and accuracy. Experimental results show that the PNLF model significantly outperforms state-of-the-art tensor completion models in both accuracy and efficiency. By addressing data loss issues, this study enhances load disaggregation precision and optimizes energy management, providing reliable data support for smart grid applications and policy formulation.
- Revuelta Herrero, J., Lozano Murciego, Á., López Barriuso, A., Iglesia, D., Villarrubia González, G., Corchado Rodríguez, J.M., Carreira, R.: Non intrusive load monitoring (nilm): A state of the art, 125–138 (2018). Springer Alcala et al. [2017] Alcala, J.M., Urena, J., Hernandez, A., Gualda, D.: Sustainable homecare monitoring system by sensing electricity data. IEEE Sensors Journal 17(23), 7741–7749 (2017) Ruano et al. [2019] Ruano, A., Hernandez, A., Ureña, J., Ruano, M., Garcia, J.: Nilm techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11), 2203 (2019) Amritkar and Kumar [1995] Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Alcala, J.M., Urena, J., Hernandez, A., Gualda, D.: Sustainable homecare monitoring system by sensing electricity data. IEEE Sensors Journal 17(23), 7741–7749 (2017) Ruano et al. [2019] Ruano, A., Hernandez, A., Ureña, J., Ruano, M., Garcia, J.: Nilm techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11), 2203 (2019) Amritkar and Kumar [1995] Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ruano, A., Hernandez, A., Ureña, J., Ruano, M., Garcia, J.: Nilm techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11), 2203 (2019) Amritkar and Kumar [1995] Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. 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Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ruano, A., Hernandez, A., Ureña, J., Ruano, M., Garcia, J.: Nilm techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11), 2203 (2019) Amritkar and Kumar [1995] Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. 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[2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. 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In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. 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IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Ruano, A., Hernandez, A., Ureña, J., Ruano, M., Garcia, J.: Nilm techniques for intelligent home energy management and ambient assisted living: A review. Energies 12(11), 2203 (2019) Amritkar and Kumar [1995] Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Amritkar, R.E., Kumar, P.P.: Interpolation of missing data using nonlinear and chaotic system analysis. Journal of Geophysical Research: Atmospheres 100(D2), 3149–3154 (1995) Allik and Annuk [2017] Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Allik, A., Annuk, A.: Interpolation of intra-hourly electricity consumption and production data, 131–136 (2017) Miao et al. [2016] Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. 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Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). 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[2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. 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[2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. 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IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. 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IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Miao, X., Gao, Y., Chen, G., Zheng, B., Cui, H.: Processing incomplete k nearest neighbor search. IEEE Transactions on Fuzzy Systems 24(6), 1349–1363 (2016) Liu et al. [2013] Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. 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[2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 208–220 (2013) [8] Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. 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International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Factorization strategies for third-order tensors. Linear Algebra and its Applications 435(3), 641–658 (2011). Special Issue: Dedication to Pete Stewart on the occasion of his 70th birthday Kilmer et al. [2013] Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. 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[2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. 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Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kilmer, M.E., Braman, K., Hao, N., Hoover, R.C.: Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM Journal on Matrix Analysis and Applications 34(1), 148–172 (2013) Navasca et al. [2010] Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). 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International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. 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[2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Navasca, C., Opperman, M., Penderghest, T., Tamon, C.: Tensors as module homomorphisms over group rings. arXiv preprint arXiv:1005.1894 (2010) Zhang et al. [2014] Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. 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IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Zhang, Z., Ely, G., Aeron, S., Hao, N., Kilmer, M.: Novel methods for multilinear data completion and de-noising based on tensor-svd, 3842–3849 (2014) Zhang and Aeron [2017] Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, Z., Aeron, S.: Exact tensor completion using t-svd. IEEE Transactions on Signal Processing 65(6), 1511–1526 (2017) Liu and Shang [2013] Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Liu, Y., Shang, F.: An efficient matrix factorization method for tensor completion. IEEE Signal Processing Letters 20(4), 307–310 (2013) Zhou et al. [2018] Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. 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Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. 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Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. 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IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. 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[2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. 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IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhou, P., Lu, C., Lin, Z., Zhang, C.: Tensor factorization for low-rank tensor completion. IEEE Transactions on Image Processing 27(3), 1152–1163 (2018) Tucker [1963] Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. 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In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Tucker, L.R.: Implications of factor analysis of three-way matrices for measurement of change, 122–137 (1963) Hitchcock [1927] Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics 6(1-4), 164–189 (1927) Harshman [1970] Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an ”explanatory” multi-model factor analysis. (1970) Carroll and Chang [1970] Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. 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In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. 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IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. 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Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970) Song et al. [2019] Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. 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[2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Song, Q., Ge, H., Caverlee, J., Hu, X.: Tensor completion algorithms in big data analytics. ACM Trans. Knowl. Discov. Data 13(1) (2019) Ji et al. [2019] Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Ji, Y., Wang, Q., Li, X., Liu, J.: A survey on tensor techniques and applications in machine learning. IEEE Access 7, 162950–162990 (2019) https://doi.org/10.1109/ACCESS.2019.2949814 Zhang et al. [2014] Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal qos-aware web service recommendation via non-negative tensor factorization (2014) Luo et al. [2020] Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. 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[2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Luo, X., Wu, H., Yuan, H., Zhou, M.: Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50(5), 1798–1809 (2020) Wu et al. [2019] Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Wu, Y., Tan, H., Li, Y., Zhang, J., Chen, X.: A fused cp factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems 30(3), 751–764 (2019) https://doi.org/10.1109/TNNLS.2018.2851612 Wang et al. [2019] Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, Q., Chen, M., Shang, M., Luo, X.: A momentum-incorporated latent factorization of tensors model for temporal-aware qos missing data prediction. Neurocomputing 367, 299–307 (2019) Wu et al. [2022a] Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Wu, H., Luo, X., Zhou, M., Rawa, M.J., Sedraoui, K., Albeshri, A.: A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA Journal of Automatica Sinica 9(3), 533–546 (2022) Wu et al. [2022b] Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, H., Luo, X., Zhou, M.: Advancing non-negative latent factorization of tensors with diversified regularization schemes. IEEE Transactions on Services Computing 15(3), 1334–1344 (2022) Luo et al. [2021] Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Luo, X., Wang, Z., Shang, M.: An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(6), 3522–3532 (2021) https://doi.org/10.1109/TSMC.2019.2930525 An et al. [2018] An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- An, W., Wang, H., Sun, Q., Xu, J., Dai, Q., Zhang, L.: A pid controller approach for stochastic optimization of deep networks, 8522–8531 (2018) Wang et al. [2020] Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Wang, H., Luo, Y., An, W., Sun, Q., Xu, J., Zhang, L.: Pid controller-based stochastic optimization acceleration for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 31(12), 5079–5091 (2020) [30] A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427, 29–39 (2021) Koren et al. [2009] Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. 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Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. 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Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) https://doi.org/10.1109/MC.2009.263 Pero and Horváth [2013] Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. 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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction, 1–13 (2013). Springer Li et al. [2017] Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. 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[2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47(1), 52–66 (2017) Wu et al. [2017] Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. 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In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, W., Zhao, J., Zhang, C., Meng, F., Zhang, Z., Zhang, Y., Sun, Q.: Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowledge-Based Systems 128, 71–77 (2017) Batra et al. [2013] Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. 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[2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. 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Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: Insights into home energy consumption in india, 1–8 (2013) Kolter and Johnson [2011] Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kolter, J.Z., Johnson, M.J.: Redd : A public data set for energy disaggregation research (2011) Kelly and Knottenbelt [2014] Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. 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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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[2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Kelly, J., Knottenbelt, W.J.: The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes. Scientific Data 2 (2014) Zheng et al. [2010] Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Zheng, V., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, pp. 236–241 (2010) Jain and Oh [2014] Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Jain, P., Oh, S.: Provable tensor factorization with missing data. Advances in Neural Information Processing Systems 27 (2014) Wu et al. [2020] Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, D., Jin, L., Luo, X.: Pmlf: Prediction-sampling-based multilayer-structured latent factor analysis. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 671–680 (2020) Luo et al. [2021a] Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. 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[2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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[2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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[2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Luo, X., Wang, D., Zhou, M., Yuan, H.: Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(2), 916–926 (2021) https://doi.org/10.1109/TSMC.2018.2884191 . Publisher Copyright: © 2013 IEEE. Luo et al. [2021b] Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. 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Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12), 9756–9773 (2021) Wu et al. [2023] Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Wu, F., Li, C., Li, Y., Tang, N.: Robust low-rank tensor completion via new regularized model with approximate svd. Information Sciences 629, 646–666 (2023) Chen et al. [2024] Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. 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[2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Chen, H., Lin, M., Liu, J., Yang, H., Zhang, C., Xu, Z.: Nt-dptc: a non-negative temporal dimension preserved tensor completion model for missing traffic data imputation. Information Sciences 653, 119797 (2024) Krizhevsky et al. [2012] Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012) Arora et al. [2019] Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Arora, S., Cohen, N., Hu, W., Luo, Y.: Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems 32 (2019) Xue et al. [2017] Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017). Melbourne, Australia Mirjalili et al. [2014] Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Advances in engineering software 69, 46–61 (2014) Rao et al. [2011] Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design 43(3), 303–315 (2011) Mirjalili and Lewis [2016] Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
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- Mirjalili, S., Lewis, A.: The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016) Wang and Liu [2020] Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Wang, J., Liu, L.: A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. International Journal of Cognitive Computing in Engineering 1, 70–77 (2020) https://doi.org/10.1016/j.ijcce.2020.11.002 Luo et al. [2023] Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Luo, X., Wu, H., Li, Z.: Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35(6), 6148–6166 (2023) Phan et al. [2020] Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer
- Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavskỳ, P., Glukhov, V., Oseledets, I., Cichocki, A.: Stable low-rank tensor decomposition for compression of convolutional neural network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp. 522–539 (2020). Springer