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DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking (2404.00243v2)

Published 30 Mar 2024 in cs.IR

Abstract: Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to select pre-defined scenarios, which may hinder the downstream ranking performance. In addition, in the academic community, the multi-scenario ranking works only come from other fields, and there are no works specifically focusing on route data due to lacking a publicly available MSRR dataset. Moreover, all the existing multi-scenario works still fail to address the three specific challenges of MSRR simultaneously, i.e. explosion of scenario number, high entanglement, and high-capacity demand. Different from the prior, to address MSRR, our key idea is to factorize the complicated scenario in route ranking into several disentangled factor scenario patterns. Accordingly, we propose a novel method, Disentangled Scenario Factorization Network (DSFNet), which flexibly composes scenario-dependent parameters based on a high-capacity multi-factor-scenario-branch structure. Then, a novel regularization is proposed to induce the disentanglement of factor scenarios. Furthermore, two extra novel techniques, i.e. scenario-aware batch normalization and scenario-aware feature filtering, are developed to improve the network awareness of scenario representation. Additionally, to facilitate MSRR research in the academic community, we propose MSDR, the first large-scale publicly available annotated industrial Multi-Scenario Driving Route dataset. Comprehensive experimental results demonstrate the superiority of our DSFNet, which has been successfully deployed in AMap to serve the major online traffic.

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References (36)
  1. Sheldon Jay Axler. 1997. Linear algebra done right. Springer.
  2. CAN: Feature Co-Action Network for Click-Through Rate Prediction. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22). Association for Computing Machinery, New York, NY, USA, 57–65. https://doi.org/10.1145/3488560.3498435
  3. Rich Caruana. 1997. Multitask Learning. Machine Learning 28 (07 1997). https://doi.org/10.1023/A:1007379606734
  4. Brian Donovan and Dan Work. 2016. New York City Taxi Trip Data (2010-2013). https://doi.org/10.13012/J8PN93H8. Accessed: 2023-07-25.
  5. Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314 (2013).
  6. GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction. CoRR abs/2007.03519 (2020). arXiv:2007.03519 https://arxiv.org/abs/2007.03519
  7. Sergey Ioffe and Christian Sze. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, Vol. 37. 448–456.
  8. Adaptive Mixtures of Local Experts. Neural Computation 3, 1 (1991), 79–87. https://doi.org/10.1162/neco.1991.3.1.79
  9. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
  10. DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (Singapore, Singapore) (WSDM ’23). Association for Computing Machinery, New York, NY, USA, 384–392. https://doi.org/10.1145/3539597.3570420
  11. Prototypical Contrastive Learning of Unsupervised Representations. arXiv:2005.04966 [cs.CV]
  12. Improving Multi-Scenario Learning to Rank in E-Commerce by Exploiting Task Relationships in the Label Space. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). Association for Computing Machinery, New York, NY, USA, 2605–2612. https://doi.org/10.1145/3340531.3412713
  13. Learning towards Minimum Hyperspherical Energy. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., 6222–6233. https://proceedings.neurips.cc/paper_files/paper/2018/file/177540c7bcb8db31697b601642eac8d4-Paper.pdf
  14. Modeling Task Relationships in Multi-Task Learning with Multi-Gate Mixture-of-Experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1930–1939. https://doi.org/10.1145/3219819.3220007
  15. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 1135–1144. https://doi.org/10.1145/2939672.2939778
  16. CRAWDAD ufrj/london-trajectories. https://dx.doi.org/10.15783/ejej-de94. Accessed: 2023-07-25.
  17. Reducing Duplicate Filters in Deep Neural Networks. In NIPS workshop on Deep Learning: Bridging Theory and Practice.
  18. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017).
  19. SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 4094–4103. https://doi.org/10.1145/3459637.3481948
  20. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 4104–4113. https://doi.org/10.1145/3459637.3481941
  21. Luigi Libero Lucio Starace. 2020. Porto taxi trajectories. https://figshare.com/articles/dataset/Porto_taxi_trajectories/12302165. Accessed: 2023-07-25.
  22. Pieter Merkus Lambertus Tammes. 1930. On the origin of number and arrangement of the places of exit on the surface of pollen-grains. Ph. D. Dissertation. University of Groningen. Relation: http://www.rug.nl/ Rights: De Bussy.
  23. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 269–278. https://doi.org/10.1145/3383313.3412236
  24. CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22). Association for Computing Machinery, New York, NY, USA, 4090–4099. https://doi.org/10.1145/3534678.3539221
  25. MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 19099–19110. https://proceedings.neurips.cc/paper_files/paper/2020/file/dcd2f3f312b6705fb06f4f9f1b55b55c-Paper.pdf
  26. MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (Singapore, Singapore) (WSDM ’23). Association for Computing Machinery, New York, NY, USA, 517–525. https://doi.org/10.1145/3539597.3570414
  27. APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 24740–24752. https://proceedings.neurips.cc/paper_files/paper/2022/file/9cd0c57170f48520749d5ae62838241f-Paper-Conference.pdf
  28. Ling Yan and Wu-Jun Li. 2014. Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 (Beijing, China) (ICML’14). JMLR.org, II–802–II–810.
  29. AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 4635–4639. https://doi.org/10.1145/3511808.3557541
  30. Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22). Association for Computing Machinery, New York, NY, USA, 1368–1376. https://doi.org/10.1145/3488560.3498479
  31. Deep Long-Tailed Learning: A Survey. arXiv:2110.04596 [cs.CV]
  32. Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 3674–3683. https://doi.org/10.1145/3511808.3557154
  33. Yu Zheng. 2011. T-Drive trajectory data sample. https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/ T-Drive sample dataset.
  34. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1059–1068. https://doi.org/10.1145/3219819.3219823
  35. Hinet: Novel multi-scenario & multi-task learning with hierarchical information extraction. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2969–2975.
  36. Automatic Expert Selection for Multi-Scenario and Multi-Task Search. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 1535–1544. https://doi.org/10.1145/3477495.3531942

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