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A Systematical Evaluation for Next-Basket Recommendation Algorithms (2209.02892v2)

Published 7 Sep 2022 in cs.IR

Abstract: Next basket recommender systems (NBRs) aim to recommend a user's next (shopping) basket of items via modeling the user's preferences towards items based on the user's purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.

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References (41)
  1. H. Hu, X. He, J. Gao, and Z. Zhang, “Modeling personalized item frequency information for next-basket recommendation,” in SIGIR, 2020, pp. 1071–1080.
  2. Y. Qin, P. Wang, and C. Li, “The world is binary: Contrastive learning for denoising next basket recommendation,” in SIGIR, 2021, pp. 859–868.
  3. W. Lu, R. Wang, S. Wang, X. Peng, H. Wu, and Q. Zhang, “Aspect-driven user preference and news representation learning for news recommendation,” IEEE Transactions on Intelligent Transportation Systems, 2022.
  4. Q. Zhang, S. Wang, W. Lu, C. Feng, X. Peng, and Q. Wang, “Rethinking adjacent dependency in session-based recommendations,” in PAKDD, 2022, pp. 301–313.
  5. W. Lu, F. Meng, S. Wang, G. Zhang, X. Zhang, A. Ouyang, and X. Zhang, “Graph-based chinese word sense disambiguation with multi-knowledge integration,” Computers, Materials & Continua, vol. 61, no. 1, pp. 197–212, 2019.
  6. W. Lu, R. Yu, S. Wang, C. Wang, P. Jian, and H. Huang, “Sentence semantic matching based on 3d cnn for human–robot language interaction,” ACM Transactions on Internet Technology, vol. 21, no. 4, pp. 1–24, 2021.
  7. S. Wang, L. Cao, Y. Wang, Q. Z. Sheng, M. A. Orgun, and D. Lian, “A survey on session-based recommender systems,” ACM Computing Surveys, vol. 54, no. 7, pp. 1–38, 2021.
  8. W. Song, S. Wang, Y. Wang, and S. Wang, “Next-item recommendations in short sessions,” in RecSys, 2021, pp. 282–291.
  9. N. Wang, S. Wang, Y. Wang, Q. Z. Sheng, and M. Orgun, “Modelling local and global dependencies for next-item recommendations,” in WISE, 2020, pp. 285–300.
  10. S. Wang, L. Hu, Y. Wang, Q. Z. Sheng, M. Orgun, and L. Cao, “Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks,” in IJCAI, 2019, pp. 3771–3777.
  11. S. Wang, L. Hu, L. Cao, X. Huang, D. Lian, and W. Liu, “Attention-based transactional context embedding for next-item recommendation,” in AAAI, 2018, pp. 2532–2539.
  12. S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in WWW, 2010, pp. 811–820.
  13. S. Wan, Y. Lan, P. Wang, J. Guo, J. Xu, and X. Cheng, “Next basket recommendation with neural networks,” in RecSys (Poster), 2015.
  14. P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng, “Learning hierarchical representation model for next-basket recommendation,” in SIGIR, 2015, pp. 403–412.
  15. F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A dynamic recurrent model for next basket recommendation,” in SIGIR, 2016, pp. 729–732.
  16. H. Hu and X. He, “Sets2Sets: Learning from sequential sets with neural networks,” in SIGKDD, 2019, pp. 1491–1499.
  17. D.-T. Le, H. W. Lauw, and Y. Fang, “Correlation-sensitive next-basket recommendation,” in IJCAI, 2019, pp. 2808–2814.
  18. T. Bai, J.-Y. Nie, W. X. Zhao, Y. Zhu, P. Du, and J.-R. Wen, “An attribute-aware neural attentive model for next basket recommendation,” in SIGIR, 2018, pp. 1201–1204.
  19. S. Wang, L. Hu, Y. Wang, Q. Z. Sheng, M. Orgun, and L. Cao, “Intention Nets: Psychology-inspired user choice behavior modeling for next-basket prediction,” in AAAI, 2020, pp. 6259–6266.
  20. S. Wang, L. Hu, Y. Wang, Q. Z. Sheng, M. Orgun, and et al., “Intention2Basket: A neural intention-driven approach for dynamic next-basket planning,” in IJCAI, 2020, pp. 2333–2339.
  21. G. Faggioli, M. Polato, and F. Aiolli, “Recency aware collaborative filtering for next basket recommendation,” in UMAP, 2020, pp. 80–87.
  22. S. Wang, X. Zhang, Y. Wang, H. Liu, and F. Ricci, “Trustworthy recommender systems,” arXiv preprint arXiv:2208.06265, pp. 1–16, 2022.
  23. M. Beladev, L. Rokach, and B. Shapira, “Recommender systems for product bundling,” Knowledge-Based Systems, vol. 111, no. C, pp. 193–206, 2016.
  24. J. Bai, C. Zhou, J. Song, X. Qu, W. An, Z. Li, and J. Gao, “Personalized bundle list recommendation,” in WWW, 2019, pp. 60–71.
  25. J. Hao, T. Zhao, J. Li, X. L. Dong, C. Faloutsos, Y. Sun, and W. Wang, “P-companion: A principled framework for diversified complementary product recommendation,” in CIKM, 2020, pp. 2517–2524.
  26. L. Chen, Y. Liu, X. He, L. Gao, and Z. Zheng, “Matching user with item set: Collaborative bundle recommendation with deep attention network,” in IJCAI, 2019, pp. 2095–2101.
  27. S. Wang, L. Cao, L. Hu, S. Berkovsky, X. Huang, L. Xiao, and W. Lu, “Hierarchical attentive transaction embedding with intra-and inter-transaction dependencies for next-item recommendation,” IEEE Intelligent Systems, vol. 36, no. 4, pp. 56–64, 2020.
  28. M. Ludewig and D. Jannach, “Evaluation of session-based recommendation algorithms,” User Modeling and User-Adapted Interaction, vol. 28, no. 4, pp. 331–390, 2018.
  29. S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. Orgun, “Sequential recommender systems: Challenges, progress and prospects,” in IJCAI, 2019, pp. 6332–6338.
  30. H. Fang, G. Guo, D. Zhang, and Y. Shu, “Deep learning-based sequential recommender systems: Concepts, algorithms, and evaluations,” in Proceedings of International Conference on Web Engineering, 2019, pp. 574–577.
  31. W. Guo, S. Wang, W. Lu, H. Wu, Q. Zhang, and Z. Shao, “Sequential dependency enhanced graph neural networks for session-based recommendations,” in DSAA, 2021, pp. 1–10.
  32. L. Hu, L. Cao, S. Wang, G. Xu, J. Cao, and Z. Gu, “Diversifying personalized recommendation with user-session context,” in IJCAI, 2017, pp. 1858–1864.
  33. N. Wang, S. Wang, Y. Wang, Q. Z. Sheng, and M. A. Orgun, “Exploiting intra- and inter-session dependencies for session-based recommendations,” World Wide Web Journal, vol. 25, no. 1, pp. 425–443, 2022.
  34. H. Fang, D. Zhang, Y. Shu, and G. Guo, “Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations,” ACM Transactions on Information Systems, vol. 39, no. 1, pp. 1–42, 2020.
  35. R. Guidotti, G. Rossetti, L. Pappalardo, F. Giannotti, and D. Pedreschi, “Market basket prediction using user-centric temporal annotated recurring sequences,” in ICDM, 2017, pp. 895–900.
  36. L. Yu, L. Sun, B. Du, C. Liu, H. Xiong, and W. Lv, “Predicting temporal sets with deep neural networks,” in SIGKDD, 2020, pp. 1083–1091.
  37. Z. Sun, D. Yu, H. Fang, J. Yang, X. Qu, J. Zhang, and C. Geng, “Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison,” in RecSys, 2020, pp. 23–32.
  38. H. Ying, F. Zhuang, F. Zhang, Y. Liu, G. Xu, X. Xie, H. Xiong, and J. Wu, “Sequential recommender system based on hierarchical attention networks,” in IJCAI, 2018, pp. 3926–3932.
  39. Y. Shen, B. Ou, and R. Li, “Mbn: Towards multi-behavior sequence modeling for next basket recommendation,” TKDD, vol. 16, no. 5, pp. 1–23, 2022.
  40. M. Ariannezhad, S. Jullien, M. Li, M. Fang, S. Schelter, and M. de Rijke, “ReCANet: A repeat consumption-aware neural network for next basket recommendation in grocery shopping,” in SIGIR, 2022.
  41. Z. Liu, X. Li, Z. Fan, S. Guo, K. Achan, and P. S. Yu, “Basket recommendation with multi-intent translation graph neural network,” in IEEE Big Data, 2020, pp. 728–737.
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