Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation (2402.13840v1)

Published 21 Feb 2024 in cs.IR and cs.AI

Abstract: Traditional session-based recommendation (SBR) utilizes session behavior sequences from anonymous users for recommendation. Although this strategy is highly efficient, it sacrifices the inherent semantic information of the items, making it difficult for the model to understand the true intent of the session and resulting in a lack of interpretability in the recommended results. Recently, LLMs have flourished across various domains, offering a glimpse of hope in addressing the aforementioned challenges. Inspired by the impact of LLMs, research exploring the integration of LLMs with the Recommender system (RS) has surged like mushrooms after rain. However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR. To address the aforementioned challenges, we have proposed the LLM Integration Framework for SBR (LLM4SBR). Serving as a lightweight and plug-and-play framework, LLM4SBR adopts a two-step strategy. Firstly, we transform session data into a bimodal form of text and behavior. In the first step, leveraging the inferential capabilities of LLMs, we conduct inference on session text data from different perspectives and design the component for auxiliary enhancement. In the second step, the SBR model is trained on behavior data, aligning and averaging two modal session representations from different perspectives. Finally, we fuse session representations from different perspectives and modalities as the ultimate session representation for recommendation. We conducted experiments on two real-world datasets, and the results demonstrate that LLM4SBR significantly improves the performance of traditional SBR models and is highly lightweight and efficient, making it suitable for industrial deployment.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
  2. Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 6 (2005), 734–749.
  3. Qwen Technical Report. arXiv preprint arXiv:2309.16609 (2023).
  4. Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 352–361.
  5. KR1442 Chowdhary and KR Chowdhary. 2020. Natural language processing. Fundamentals of artificial intelligence (2020), 603–649.
  6. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084 (2022).
  7. Uncovering ChatGPT’s Capabilities in Recommender Systems. arXiv preprint arXiv:2305.02182 (2023).
  8. The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems. 293–296.
  9. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
  10. Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524 (2023).
  11. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems. 299–315.
  12. VIP5: Towards Multimodal Foundation Models for Recommendation. arXiv preprint arXiv:2305.14302 (2023).
  13. Leveraging large language models for sequential recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems. 1096–1102.
  14. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173–182.
  15. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
  16. How to Index Item IDs for Recommendation Foundation Models. arXiv preprint arXiv:2305.06569 (2023).
  17. Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. arXiv preprint arXiv:2305.06474 (2023).
  18. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474.
  19. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428.
  20. GPT4Rec: A generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.03879 (2023).
  21. Prompt distillation for efficient llm-based recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1348–1357.
  22. CTRL: Connect Tabular and Language Model for CTR Prediction. arXiv preprint arXiv:2306.02841 (2023).
  23. E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation. arXiv preprint arXiv:2312.02443 (2023).
  24. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1997–2002.
  25. M6: Multi-modality-to-multi-modality multitask mega-transformer for unified pretraining. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3251–3261.
  26. STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1831–1839.
  27. Recommender system application developments: a survey. Decision support systems 74 (2015), 12–32.
  28. Danilo Mandic and Jonathon Chambers. 2001. Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley.
  29. James R Norris. 1998. Markov chains. Number 2. Cambridge university press.
  30. Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2075–2084.
  31. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811–820.
  32. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61–80.
  33. Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation. In Proceedings of the ACM Web Conference 2023. 165–176.
  34. Large Language Models for Intent-Driven Session Recommendations. arXiv preprint arXiv:2312.07552 (2023).
  35. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
  36. Towards representation alignment and uniformity in collaborative filtering. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1816–1825.
  37. A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. 345–354.
  38. Exploiting intra-and inter-session dependencies for session-based recommendations. World Wide Web 25, 1 (2022), 425–443.
  39. Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 169–178.
  40. Llmrec: Large language models with graph augmentation for recommendation. arXiv preprint arXiv:2311.00423 (2023).
  41. Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 346–353.
  42. Self-supervised hypergraph convolutional networks for session-based recommendation. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 4503–4511.
  43. Graph contextualized self-attention network for session-based recommendation.. In IJCAI, Vol. 19. 3940–3946.
  44. TAGNN: target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 1921–1924.
  45. Where to go next for recommender systems? id-vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835 (2023).
  46. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414 (2022).
  47. On generative agents in recommendation. arXiv preprint arXiv:2310.10108 (2023).
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Shutong Qiao (3 papers)
  2. Chen Gao (136 papers)
  3. Junhao Wen (22 papers)
  4. Wei Zhou (308 papers)
  5. Qun Luo (5 papers)
  6. Peixuan Chen (4 papers)
  7. Yong Li (628 papers)
Citations (1)
View on Semantic Scholar