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Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation

arXiv 2311.09049 ↗First posted 15 Nov 2023cs.IRcited by 171

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Abstract (arXiv)

Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there exists a large semantic gap between LLMs and recommender systems, since items to be recommended are often indexed by discrete identifiers (item ID) out of the LLM's vocabulary. In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. Our approach can directly generate items from the entire item set for recommendation, without relying on candidate items. Specifically, we make two major contributions in our approach. For item indexing, we design a learning-based vector quantization method with uniform semantic mapping, which can assign meaningful and non-conflicting IDs (called item indices) for items. For alignment tuning, we propose a series of specially designed tuning tasks to enhance the integration of collaborative semantics in LLMs. Our fine-tuning tasks enforce LLMs to deeply integrate language and collaborative semantics (characterized by the learned item indices), so as to achieve an effective adaptation to recommender systems. Extensive experiments demonstrate the effectiveness of our method, showing that our approach can outperform a number of competitive baselines including traditional recommenders and existing LLM-based recommenders. Our code is available at https://github.com/RUCAIBox/LC-Rec/.

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Nov 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 29; 2025: 66; 2026: 76.

171 papers.

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  1. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  2. 2024User-LLM: Efficient LLM Contextualization with User Embeddingscited by 74
  3. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  4. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  5. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendationcited by 44
  6. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  7. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  8. 2024Large Language Models and Video Games: A Preliminary Scoping Reviewcited by 23
  9. 2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19
  10. 2024LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendationcited by 19
  11. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  12. 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendationcited by 18
38 more
  1. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  2. 2024Towards Efficient and Effective Unlearning of Large Language Models for Recommendationcited by 16
  3. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 16
  4. 2024Unifying Generative and Dense Retrieval for Sequential Recommendationcited by 16
  5. 2025MiniOneRec: An Open-Source Framework for Scaling Generative Recommendationcited by 16
  6. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 14
  7. 2024Ads Recommendation in a Collapsed and Entangled Worldcited by 13
  8. 2025Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrievalcited by 13
  9. 2025Multimodal Quantitative Language for Generative Recommendationcited by 12
  10. 2024Efficient Inference for Large Language Model-based Generative Recommendationcited by 11
  11. 2025HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDscited by 11
  12. 2025MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptationcited by 10
  13. 2025CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendationcited by 10
  14. 2024Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenizationcited by 9
  15. 2025Improving LLM-powered Recommendations with Personalized Informationcited by 9
  16. 2025LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendationcited by 9
  17. 2024Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanismcited by 8
  18. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  19. 2024Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendationcited by 8
  20. 2025Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenizationcited by 8
  21. 2025RecGPT-V2 Technical Reportcited by 8
  22. 2024CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendationcited by 7
  23. 2025Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendationcited by 7
  24. 2025Universal Item Tokenization for Transferable Generative Recommendationcited by 7
  25. 2025GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusioncited by 7
  26. 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Surveycited by 7
  27. 2026Generative Recommendation for Large-Scale Advertisingcited by 7
  28. 2024Preference Discerning with LLM-Enhanced Generative Retrievalcited by 6
  29. 2025EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integrationcited by 6
  30. 2025Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendationcited by 6
  31. 2026RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systemscited by 6
  32. 2025Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified Viewcited by 5
  33. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5
  34. 2025KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendationcited by 5
  35. 2025TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarioscited by 5
  36. 2025OneLoc: Geo-Aware Generative Recommender Systems for Local Life Servicecited by 5
  37. 2025NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendationscited by 5
  38. 2025Masked Diffusion for Generative Recommendationcited by 5

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