Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering (2405.09042v1)

Published 15 May 2024 in cs.IR

Abstract: Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling method is to abstract the intent into unknowable but learnable shared or non-shared parameters. Despite considerable progress, we argue that it still confronts the following challenges: firstly, these methods only capture the coarse-grained aspects of intent, ignoring the fact that user-item interactions will be affected by collective and individual factors (e.g., a user may choose a movie because of its high box office or because of his own unique preferences); secondly, modeling believable intent is severely hampered by implicit feedback, which is incredibly sparse and devoid of true semantics. To address these challenges, we propose a novel recommendation framework designated as Bilateral Intent-guided Graph Collaborative Filtering (BIGCF). Specifically, we take a closer look at user-item interactions from a causal perspective and put forth the concepts of individual intent-which signifies private preferences-and collective intent-which denotes overall awareness. To counter the sparsity of implicit feedback, the feature distributions of users and items are encoded via a Gaussian-based graph generation strategy, and we implement the recommendation process through bilateral intent-guided graph reconstruction re-sampling. Finally, we propose graph contrastive regularization for both interaction and intent spaces to uniformize users, items, intents, and interactions in a self-supervised and non-augmented paradigm. Experimental results on three real-world datasets demonstrate the effectiveness of BIGCF compared with existing solutions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In The Eleventh International Conference on Learning Representations (ICLR).
  2. Latent User Intent Modeling for Sequential Recommenders. In Companion Proceedings of the ACM Web Conference 2023 (WWW). 427–431.
  3. Curriculum Disentangled Recommendation with Noisy Multi-feedback. Advances in Neural Information Processing Systems (NeurIPS) 34, 26924–26936.
  4. Attentive Collaborative Filtering: Multimedia recommendation with Item-and Component-level Attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 335–344.
  5. A Simple Framework for Contrastive Learning of Visual Representations. In International Conference on Machine Learning (ICML). 1597–1607.
  6. Intent Contrastive Learning for Sequential Recommendation. In Proceedings of the ACM Web Conference 2022 (WWW). 2172–2182.
  7. Xavier Glorot and Yoshua Bengio. 2010. Understanding the Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (ICAIS). 249–256.
  8. Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 355–364.
  9. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 639–648.
  10. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW). 173–182.
  11. Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS) 33, 6840–6851.
  12. Understanding Dimensional Collapse in Contrastive Self-supervised Learning. arXiv preprint arXiv:2110.09348 (2021).
  13. Diederik P Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014).
  14. Diederik P Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. arXiv preprint arXiv:1312.6114 (2013).
  15. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In The Fifth International Conference on Learning Representations (ICLR).
  16. Norman Knyazev and Harrie Oosterhuis. 2022. The Bandwagon Effect: Not Just Another Bias. In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR). 243–253.
  17. Disentangled Contrastive Learning on Graphs. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 34. 21872–21884.
  18. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW). 689–698.
  19. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In Proceedings of the ACM Web Conference 2022 (WWW). 2320–2329.
  20. Disentangled Graph Convolutional Networks. In International Conference on Machine Learning (ICML). 4212–4221.
  21. Learning Disentangled Representations for Recommendation. Advances in Neural Information Processing Systems (NeurIPS) 32, 5711–5722.
  22. Disentangled Contrastive Collaborative Filtering. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1137–1146.
  23. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452–461.
  24. Introduction to Recommender Systems Handbook. In Recommender Systems Handbook. Springer, 1–35.
  25. Autorec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web (WWW). 111–112.
  26. Bilateral Variational Autoencoder for Collaborative Filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM). 292–300.
  27. Feng Wang and Huaping Liu. 2021. Understanding the Behaviour of Contrastive Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2495–2504.
  28. Tongzhou Wang and Phillip Isola. 2020. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. In International Conference on Machine Learning (ICML). 9929–9939.
  29. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 165–174.
  30. Learning Intents behind Interactions with Knowledge Graph for Recommendation. In Proceedings of the Web Conference 2021 (WWW). 878–887.
  31. Disentangled Graph Collaborative Filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1001–1010.
  32. DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 1605–1614.
  33. Model-agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD). 1791–1800.
  34. Self-Supervised Graph Learning for Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 726–735.
  35. A Neural Influence Diffusion Model for Social Recommendation. In Proceedings of the 42nd international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 235–244.
  36. Dual Intents Graph Modeling for User-centric Group Discovery. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM). 2716–2725.
  37. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 32, 1 (2020), 4–24.
  38. Hypergraph Contrastive Collaborative Filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 70–79.
  39. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR Conference on Research and Development in Information Retrieval. 1434–1443.
  40. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 974–983.
  41. Graph Contrastive Learning with Augmentations. In Advances in Neural Information Processing Systems (NeurIPS). 5812–5823.
  42. Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-encoder. ACM Transactions on Information Systems (TOIS) 41, 3 (2023), 1–28.
  43. NIE-GCN: Neighbor Item Embedding-Aware Graph Convolutional Network for Recommendation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 54, 5 (2024), 2810–2821.
  44. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In Proceedings of the Web Conference 2021 (WWW). 2980–2991.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Yi Zhang (994 papers)
  2. Lei Sang (19 papers)
  3. Yiwen Zhang (87 papers)
Citations (3)
View on Semantic Scholar

Summary

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

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

Tweets