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Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models (2312.16275v2)

Published 26 Dec 2023 in cs.IR and cs.MM

Abstract: Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. However, the aspects and intents are inferred directly from user reviews or behavior patterns, suffering from the data noise and the data sparsity problem. Furthermore, it is difficult to understand the reasons behind recommendations due to the challenges of interpreting implicit aspects and intents. Inspired by the deep semantic understanding offered by LLMs, we introduce a chain-based prompting approach to uncover semantic aspect-aware interactions, which provide clearer insights into user behaviors at a fine-grained semantic level. To incorporate the abundant interactions of various aspects, we propose the simple yet effective Semantic Aspect-based Graph Convolution Network (short for SAGCN). By performing graph convolutions on multiple semantic aspect graphs, SAGCN efficiently combines embeddings across multiple semantic aspects for final user and item representations. The effectiveness of the SAGCN was evaluated on three publicly available datasets through extensive experiments, which revealed that it outperforms all other competitors. Furthermore, interpretability analysis experiments were conducted to demonstrate the interpretability of incorporating semantic aspects into the model.

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References (51)
  1. Robert M Bell and Yehuda Koren. 2007. Lessons from the netflix prize challenge. SIGKDD 9, 2 (2007), 75–79.
  2. Rose Catherine and William Cohen. 2017. TransNets: Learning to Transform for Recommendation. In RecSys. ACM, 288–296.
  3. Neural attentional rating regression with review-level explanations. In WWWC. IW3C2, 1583–1592.
  4. A33{}^{3}start_FLOATSUPERSCRIPT 3 end_FLOATSUPERSCRIPTNCF: An Adaptive Aspect Attention Model for Rating Prediction. In IJCAI . 3748–3754.
  5. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In WWW. IW3C2, 639–648.
  6. Multi-Behavior Recommendation with Cascading Graph Convolution Networks. In Proceedings of the ACM Web Conference 2023. ACM, 1181–1189.
  7. ANR: Aspect-based Neural Recommender. In CIKM. ACM, 147–156.
  8. Aminu Da’u and Naomie Salim. 2022. Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review 53, 4 (2022), 2709–2748.
  9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. ACL, 4171–4186.
  10. Graph Trend Filtering Networks for Recommendation. In SIGIR. ACM, 112–121.
  11. Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In RecSys. ACM, 299–315.
  12. Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In AAAI. AAAI Press, 144–150.
  13. TriRank: Review aware Explainable Recommendation by Modeling Aspects. In CIKM. ACM, 1661–1670.
  14. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM, 639–648.
  15. Neural collaborative filtering. In WWW. IW3C2, 173–182.
  16. GenRec: Large Language Model for Generative Recommendation. CoRR abs/2307.00457 (2023).
  17. Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. CoRR abs/2305.06474 (2023).
  18. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR. 1–15.
  19. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. OpenReview.net.
  20. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.
  21. A Capsule Network for Recommendation and Explaining What You Like and Dislike. In SIGIR. ACM, 275–284.
  22. Personalized Prompt Learning for Explainable Recommendation. TOIS 41, 4 (2023).
  23. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW. ACM, 2320–2329.
  24. Disentangled Multimodal Representation Learning for Recommendation. TMM (2022), 1–11.
  25. User Diverse Preference Modeling by Multimodal Attentive Metric Learning. In MM. ACM, 1526–1534.
  26. Interest-Aware Message-Passing GCN for Recommendation. In WWW. ACM, 1296–1305.
  27. An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation. TKDE 34, 9 (2022), 4077–4088.
  28. A First Look at LLM-Powered Generative News Recommendation. CoRR abs/2305.06566 (2023).
  29. Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. In RecSys. ACM, 165–172.
  30. Image-based recommendations on styles and substitutes. In SIGIR. ACM, 43–52.
  31. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452–461.
  32. Zero-Shot Recommendation As Language Modeling. In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II. Springer, 223–230.
  33. Rating-boosted latent topics: Understanding users and items with ratings and reviews. In IJCAI. AAAI Press.
  34. Graph Convolutional Matrix Completion. In ICSCA. ACM.
  35. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In WWW. IW3C2, 391–400.
  36. KGAT: Knowledge Graph Attention Network for Recommendation. In SIGKDD. ACM, 950–958.
  37. Neural Graph Collaborative Filtering. In SIGIR. ACM, 165–174.
  38. Disentangled Graph Collaborative Filtering. In SIGIR. ACM, 1001–1010.
  39. DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation. In CIKM. ACM, 1605–1614.
  40. Personalized Hashtag Recommendation for Micro-videos. In MM. ACM, 1446–1454.
  41. Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. In MM. ACM, 3541–3549.
  42. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning. PMLR, 6861–6871.
  43. A Survey on Large Language Models for Recommendation. CoRR abs/2305.19860 (2023).
  44. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 153–162.
  45. Deep matrix factorization models for recommender systems. In IJCAI. AAAI Press, 3203–3209.
  46. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In SIGKDD. ACM, 974–983.
  47. Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. In RecSys. ACM, 993–999.
  48. Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach. CoRR abs/2305.07001 (2023).
  49. Mining Latent Structures for Multimedia Recommendation. In TMM. ACM, 3872–3880.
  50. Joint representation learning for top-n recommendation with heterogeneous information sources. In CIKM. ACM, 1449–1458.
  51. Bootstrap Latent Representations for Multi-modal Recommendation. In WWW. IW3C2.
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