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RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation

Published 19 Sep 2023 in cs.IR | (2309.10469v1)

Abstract: Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately under the condition of data sparsity. To address this challenge, some methods have leveraged external user behavior data from multiple platforms to enrich user representation. However, all of these methods require a consistent user ID across platforms and ignore the information from similar users. In this study, we propose RUEL, a novel retrieval-based sequential recommender that can effectively incorporate external anonymous user behavior data from Edge browser logs to enhance recommendation. We first collect and preprocess a large volume of Edge browser logs over a one-year period and link them to target entities that correspond to candidate items in recommendation datasets. We then design a contrastive learning framework with a momentum encoder and a memory bank to retrieve the most relevant and diverse browsing sequences from the full browsing log based on the semantic similarity between user representations. After retrieval, we apply an item-level attentive selector to filter out noisy items and generate refined sequence embeddings for the final predictor. RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets. We conduct extensive experiments on four real datasets for sequential recommendation tasks and demonstrate that RUEL significantly outperforms state-of-the-art baselines. We also conduct ablation studies and qualitative analysis to validate the effectiveness of each component of RUEL and provide additional insights into our method.

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References (52)
  1. A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2923–2932.
  2. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
  3. SPMC: socially-aware personalized markov chains for sparse sequential recommendation. arXiv preprint arXiv:1708.04497 (2017).
  4. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.
  5. Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15750–15758.
  6. Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 51–60.
  7. Personalized travel route recommendation using collaborative filtering based on GPS trajectories. IJED 11, 3 (2018), 284–307.
  8. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
  9. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems 33 (2020), 21271–21284.
  10. Retrieval augmented language model pre-training. In International Conference on Machine Learning. PMLR, 3929–3938.
  11. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9729–9738.
  12. Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th international conference on data mining (ICDM). IEEE, 191–200.
  13. Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM international conference on information and knowledge management. 843–852.
  14. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
  15. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems. 241–248.
  16. Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 306–310.
  17. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206.
  18. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR’15.
  19. Travel route recommendation using geotags in photo sharing sites. In CIKM. ACM, 579–588.
  20. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019).
  21. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428.
  22. Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2009–2019.
  23. Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations.. In IJCAI, Vol. 15. 1778–1784.
  24. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).
  25. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021).
  26. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).
  27. Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1150–1160.
  28. Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 519–528.
  29. Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 813–823.
  30. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering. In NAACL-HLT. Association for Computational Linguistics, 5835–5847.
  31. Personalizing session-based recommendations with hierarchical recurrent neural networks. In proceedings of the Eleventh ACM Conference on Recommender Systems. 130–137.
  32. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140 (2020), 1–67.
  33. RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In EMNLP (1). Association for Computational Linguistics, 2825–2835.
  34. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
  35. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811–820.
  36. A Model-Agnostic Causal Learning Framework for Recommendation using Search Data. In Proceedings of the ACM Web Conference 2022. 224–233.
  37. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.
  38. Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining. 565–573.
  39. Attention is all you need. Advances in neural information processing systems 30 (2017).
  40. Sequential Recommendation with Multiple Contrast Signals. ACM Transactions on Information Systems (TOIS) (2022).
  41. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management. 417–426.
  42. Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7794–7803.
  43. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 950–958.
  44. Constructing popular routes from uncertain trajectories. In SIGKDD. 195–203.
  45. Neural news recommendation with heterogeneous user behavior. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 4874–4883.
  46. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726–735.
  47. Contrastive learning for sequential recommendation. arXiv preprint arXiv:2010.14395 (2020).
  48. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019).
  49. USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2373–2382.
  50. Kb4rec: A data set for linking knowledge bases with recommender systems. Data Intelligence 1, 2 (2019), 121–136.
  51. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4653–4664.
  52. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1893–1902.

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