Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework (2404.08361v2)

Published 12 Apr 2024 in cs.IR and cs.AI

Abstract: Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi-domain learning is a typical solution in this regard. While considerable efforts have been made in this regard, there are still two long-standing challenges: (1) Accurately depicting the differences among domains using domain features is crucial for enhancing the performance of each domain. However, manually designing domain features and models for numerous domains can be a laborious task. (2) Users typically have limited impressions in only a few domains. Extracting features automatically from other domains and leveraging them to improve the predictive capabilities of each domain has consistently posed a challenging problem. In this paper, we propose an Automatic Domain Feature Extraction and Personalized Integration (DFEI) framework for the large-scale multi-domain recommendation. The framework automatically transforms the behavior of each individual user into an aggregation of all user behaviors within the domain, which serves as the domain features. Unlike offline feature engineering methods, the extracted domain features are higher-order representations and directly related to the target label. Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification. Experimental results on both public and industrial datasets, consisting of over 20 domains, clearly demonstrate that the proposed framework achieves significantly better performance compared with SOTA baselines. Furthermore, we have released the source code of the proposed framework at https://github.com/xidongbo/DFEI.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Rich Caruana. 1997. Multitask Learning. Machine Learning 28 (07 1997). https://doi.org/10.1023/A:1007379606734
  2. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23). 3795–3804. https://doi.org/10.1145/3580305.3599884
  3. Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce. 2020 International Conference on Data Mining Workshops (ICDMW) (2020), 127–135. https://api.semanticscholar.org/CorpusID:229211225
  4. MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3803–3807.
  5. Multi-domain learning by confidence-weighted parameter combination. Machine Learning 79 (2010), 123–149.
  6. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22). 3953–3957. https://doi.org/10.1145/3511808.3557624
  7. Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation. arXiv preprint arXiv:2309.02061 (2023).
  8. Matt W Gardner and SR Dorling. 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment (1998).
  9. Adversarial Feature Translation for Multi-domain Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21). 2964–2973. https://doi.org/10.1145/3447548.3467176
  10. MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks. In Proceedings of the ACM Web Conference 2022 (WWW ’22). 2205–2215. https://doi.org/10.1145/3485447.3512093
  11. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). 667–676. https://doi.org/10.1145/3269206.3271684
  12. SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4175–4184.
  13. Multi-Domain Learning: When Do Domains Matter? In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
  14. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR, Vol. 5.
  15. M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework. In Proceedings of the 17th ACM Conference on Recommender Systems. 771–776.
  16. 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 (CIKM ’20). 2605–2612. https://doi.org/10.1145/3340531.3412713
  17. P. Li and Alexander Tuzhilin. 2019. DDTCDR: Deep Dual Transfer Cross Domain Recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining (2019). https://api.semanticscholar.org/CorpusID:204402822
  18. Hamur: Hyper adapter for multi-domain recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1268–1277.
  19. 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 (KDD ’18). 1930–1939. https://doi.org/10.1145/3219819.3220007
  20. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’18). 1137–1140. https://doi.org/10.1145/3209978.3210104
  21. Cross-Stitch Networks for Multi-task Learning. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3994–4003. https://doi.org/10.1109/CVPR.2016.433
  22. Multi-domain Recommendation with Embedding Disentangling and Domain Alignment. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1917–1927.
  23. Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345–1359.
  24. Latent Multi-Task Architecture Learning. Proceedings of the AAAI Conference on Artificial Intelligence 33 (07 2019), 4822–4829. https://doi.org/10.1609/aaai.v33i01.33014822
  25. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. arXiv:1701.06538 [cs.LG]
  26. 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 (CIKM ’21). 4094–4103. https://doi.org/10.1145/3459637.3481948
  27. 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 (CIKM ’21). 4104–4113. https://doi.org/10.1145/3459637.3481941
  28. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys ’20). 269–278. https://doi.org/10.1145/3383313.3412236
  29. Multi-Scenario Ranking with Adaptive Feature Learning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 517–526.
  30. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 2579–2605.
  31. PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1498–1507.
  32. Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’20). 2377–2386. https://doi.org/10.1145/3397271.3401443
  33. Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21). 3745–3755. https://doi.org/10.1145/3447548.3467071
  34. Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection. In AAAI.
  35. Neural Hierarchical Factorization Machines for User’s Event Sequence Analysis. In SIGIR. 1893–1896.
  36. Personalized Approximate Pareto-Efficient Recommendation. In Proceedings of the Web Conference 2021 (WWW ’21). 3839–3849. https://doi.org/10.1145/3442381.3450039
  37. MUSENET: Multi-scenario learning for repeat-aware personalized recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 517–525.
  38. AdaTask: a task-aware adaptive learning rate approach to multi-task learning. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence (AAAI’23/IAAI’23/EAAI’23). AAAI Press. https://doi.org/10.1609/aaai.v37i9.26275
  39. Meta-generator enhanced multi-domain recommendation. In Companion Proceedings of the ACM Web Conference 2023. 485–489.
  40. M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 5650–5659.
  41. HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction. 2023 IEEE 39th International Conference on Data Engineering (ICDE) (2023), 2969–2975. https://api.semanticscholar.org/CorpusID:257482348
  42. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. In TheWebConf. 928–938.
  43. A comprehensive survey on transfer learning. Proc. IEEE 109, 1 (2020), 43–76.
  44. Automatic Expert Selection for Multi-Scenario and Multi-Task Search. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2022). https://api.semanticscholar.org/CorpusID:249191419

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now