mlsanityData to 14 Oct 2026

Representation Learning with Large Language Models for Recommendation

arXiv 2310.15950 ↗First posted 24 Oct 2023cs.IR cs.AIcited by 155

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Abstract (arXiv)

Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. However, these graph-based recommenders heavily depend on ID-based data, potentially disregarding valuable textual information associated with users and items, resulting in less informative learned representations. Moreover, the utilization of implicit feedback data introduces potential noise and bias, posing challenges for the effectiveness of user preference learning. While the integration of large language models (LLMs) into traditional ID-based recommenders has gained attention, challenges such as scalability issues, limitations in text-only reliance, and prompt input constraints need to be addressed for effective implementation in practical recommender systems. To address these challenges, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. It proposes a recommendation paradigm that integrates representation learning with LLMs to capture intricate semantic aspects of user behaviors and preferences. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework. This work further establish a theoretical foundation demonstrating that incorporating textual signals through mutual information maximization enhances the quality of representations. In our evaluation, we integrate RLMRec with state-of-the-art recommender models, while also analyzing its efficiency and robustness to noise data. Our implementation codes are available at https://github.com/HKUDS/RLMRec.

Key facts. Cited by 155 papers in the map (data through 14 Oct 2026). Builds on 37 of its 48 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

37 of 48 references.

References · 37

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  6. 2019Neural Graph Collaborative Filteringcited by 661
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  8. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  9. 2023A Survey on Large Language Models for Recommendationcited by 379
  10. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
  11. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 360
  12. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
25 more
  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  2. 2020Self-supervised Graph Learning for Recommendationcited by 305
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  4. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  5. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  6. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  7. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  8. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  9. 2023Text Is All You Need: Learning Language Representations for Sequential Recommendationcited by 149
  10. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  11. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  12. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  13. 2022Self-Supervised Learning for Recommender Systems: A Surveycited by 95
  14. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  15. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  16. 2020Denoising Implicit Feedback for Recommendationcited by 80
  17. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  18. 2021Deconfounded Recommendation for Alleviating Bias Amplificationcited by 51
  19. 2023Debiased Contrastive Learning for Sequential Recommendationcited by 38
  20. 2023Automated Self-Supervised Learning for Recommendationcited by 37
  21. 2021DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendationcited by 28
  22. 2023Heterogeneous Graph Contrastive Learning for Recommendationcited by 28
  23. 2023Disentangled Contrastive Collaborative Filteringcited by 22
  24. 2023SSLRec: A Self-Supervised Learning Framework for Recommendationcited by 17
  25. 2021Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 55; 2025: 62; 2026: 35.

155 papers.

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  1. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 284
  2. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  3. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  4. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 78
  5. 2024A Survey of Large Language Models for Graphscited by 72
  6. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  7. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  8. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  9. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  10. 2024HiGPT: Heterogeneous Graph Language Modelcited by 45
  11. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendationcited by 44
  12. 2023DiffKG: Knowledge Graph Diffusion Model for Recommendationcited by 41
38 more
  1. 2024XRec: Large Language Models for Explainable Recommendationcited by 35
  2. 2024GraphEdit: Large Language Models for Graph Structure Learningcited by 33
  3. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  4. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendationcited by 33
  5. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  6. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  7. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  8. 2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 22
  9. 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 22
  10. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  11. 2024Measuring the Inconsistency of Large Language Models in Preferential Rankingcited by 19
  12. 2025LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendationcited by 19
  13. 2024Language-Based User Profiles for Recommendationcited by 18
  14. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  15. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  16. 2025MiniOneRec: An Open-Source Framework for Scaling Generative Recommendationcited by 17
  17. 2024Dual Test-time Training for Out-of-distribution Recommender Systemcited by 16
  18. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  19. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 12
  20. 2025Uncertainty Quantification and Decomposition for LLM-based Recommendationcited by 12
  21. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
  22. 2024AMPO: Automatic Multi-Branched Prompt Optimizationcited by 11
  23. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  24. 2024LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendationcited by 10
  25. 2024EasyRec: Simple yet Effective Language Models for Recommendationcited by 10
  26. 2024End-to-end Training for Recommendation with Language-based User Profilescited by 10
  27. 2025Improving LLM-powered Recommendations with Personalized Informationcited by 10
  28. 2025LLM-based User Profile Management for Recommender Systemcited by 10
  29. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  30. 2024Preference Diffusion for Recommendationcited by 9
  31. 2024Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenizationcited by 9
  32. 2024LOGIN: A Large Language Model Consulted Graph Neural Network Training Frameworkcited by 8
  33. 2024Enhancing High-order Interaction Awareness in LLM-based Recommender Modelcited by 8
  34. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  35. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  36. 2024Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendationcited by 7
  37. 2024Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendationcited by 7
  38. 2025A Survey on Large Language Models in Multimodal Recommender Systemscited by 7

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