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FineRec:Exploring Fine-grained Sequential Recommendation (2404.12975v1)

Published 19 Apr 2024 in cs.IR and cs.AI

Abstract: Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a LLM to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. To tackle the diversity of opinions, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several realworld datasets demonstrate the superiority of our FineRec over existing state-of-the-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.

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References (47)
  1. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM, 1007–1014.
  2. Category-aware Collaborative Sequential Recommendation. In SIGIR. 388–397.
  3. Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction. In SIGKDD, Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 66–75.
  4. Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation. In SIGIR. ACM, 352–361.
  5. Intent Contrastive Learning for Sequential Recommendation. In WWW. 2172–2182.
  6. A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction. In IJCAI. ijcai.org, 3748–3754.
  7. ANR: Aspect-based Neural Recommender. In CIKM. ACM, 147–156.
  8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171–4186.
  9. Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation. In AAAI. AAAI Press, 7667–7674.
  10. Frequency Enhanced Hybrid Attention Network for Sequential Recommendation. In SIGIR. ACM, 78–88.
  11. VIP5: Towards Multimodal Foundation Models for Recommendation. In Findings of EMNLP. Association for Computational Linguistics, 9606–9620.
  12. Session-based Recommendations with Recurrent Neural Networks. In ICLR.
  13. Towards Universal Sequence Representation Learning for Recommender Systems. In KDD. 585–593.
  14. Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems. In CIKM. ACM, 843–853.
  15. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In SIGKDD, Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel (Eds.). ACM, 168–177.
  16. Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM. 197–206.
  17. MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation. In SIGIR. ACM, 68–77.
  18. A Review-Driven Neural Model for Sequential Recommendation. In IJCAI. 2866–2872.
  19. Neural Attentive Session-based Recommendation. In CIKM. 1419–1428.
  20. Text Is All You Need: Learning Language Representations for Sequential Recommendation. In SIGKDD. ACM, 1258–1267.
  21. AutoMLP: Automated MLP for Sequential Recommendations. In WWW. ACM, 1190–1198.
  22. MLP4Rec: A Pure MLP Architecture for Sequential Recommendations. In IJCAI. 2138–2144.
  23. Multi-Intention Oriented Contrastive Learning for Sequential Recommendation. In WSDM. 411–419.
  24. A Self-Correcting Sequential Recommender. In WWW. ACM, 1283–1293.
  25. DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation. In KDD. ACM, 344–352.
  26. Distribution-based Learnable Filters with Side Information for Sequential Recommendation. In RecSys. ACM, 78–88.
  27. AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. In CIKM. ACM, 1493–1502.
  28. Text Matching Improves Sequential Recommendation by Reducing Popularity Biases. In CIKM. ACM, 1534–1544.
  29. Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey. ACM Comput. Surv. 56, 2 (2024), 30:1–30:40.
  30. Meta-optimized Contrastive Learning for Sequential Recommendation. In SIGIR. ACM, 89–98.
  31. Self-supervised Graph Disentangled Networks for Review-based Recommendation. In IJCAI. ijcai.org, 2288–2295.
  32. A Review-aware Graph Contrastive Learning Framework for Recommendation. In SIGIR. ACM, 1283–1293.
  33. CBML: A Cluster-based Meta-learning Model for Session-based Recommendation. In CIKM. 1713–1722.
  34. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In CIKM. 1441–1450.
  35. Session-Based Recommendation with Graph Neural Networks. In AAAI. 346–353.
  36. LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup. In SIGIR. 527–536.
  37. Graph Masked Autoencoder for Sequential Recommendation. In SIGIR. ACM, 321–330.
  38. Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network. In WSDM. 168–176.
  39. Dynamic intent-aware iterative denoising network for session-based recommendation. Inf. Process. Manag. 59, 3 (2022), 102936.
  40. Side Information-Driven Session-based Recommendation: A Survey. arXiv preprint arXiv:2402.17129 (2024).
  41. Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation. ACM Trans. Inf. Syst. 42, 3, Article 68 (2023), 28 pages.
  42. Beyond Co-Occurrence: Multi-Modal Session-Based Recommendation. IEEE Trans. Knowl. Data Eng. 36, 4 (2024), 1450–1462.
  43. Price DOES Matter!: Modeling Price and Interest Preferences in Session-based Recommendation. In SIGIR. 1684–1693.
  44. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In SIGIR. ACM, 83–92.
  45. Adaptive Disentangled Transformer for Sequential Recommendation. In KDD. ACM, 3434–3445.
  46. Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation. In RecSys. ACM, 141–150.
  47. Attention Calibration for Transformer-based Sequential Recommendation. In CIKM. ACM, 3595–3605.
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