mlsanityData to 7 Oct 2026

LLMRec: Large Language Models with Graph Augmentation for Recommendation

arXiv 2311.00423 ↗First posted 1 Nov 2023cs.IRcited by 171

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Abstract (arXiv)

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2023.

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Citing papers per year: 2023: 2; 2024: 60; 2025: 70; 2026: 39.

171 papers.

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  1. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  2. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  3. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  4. 2024A Survey of Large Language Models for Graphscited by 71
  5. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  6. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  7. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 56
  8. 2024Can we Soft Prompt LLMs for Graph Learning Tasks?cited by 52
  9. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 46
  10. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?cited by 46
  11. 2024HiGPT: Heterogeneous Graph Language Modelcited by 44
  12. 2023DiffKG: Knowledge Graph Diffusion Model for Recommendationcited by 41
38 more
  1. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
  2. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  3. 2024GraphEdit: Large Language Models for Graph Structure Learningcited by 33
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  6. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  7. 2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 21
  8. 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Modelscited by 20
  9. 2025LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendationcited by 19
  10. 2024SLMRec: Distilling Large Language Models into Small for Sequential Recommendationcited by 18
  11. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  12. 2024SPRec: Self-Play to Debias LLM-based Recommendationcited by 18
  13. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  14. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  15. 2026MemRec: Collaborative Memory-Augmented Agentic Recommender Systemcited by 15
  16. 2024Large Language Model Meets Graph Neural Network in Knowledge Distillationcited by 13
  17. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  18. 2025Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1Mcited by 12
  19. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  20. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  21. 2025LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendationcited by 11
  22. 2024LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendationcited by 10
  23. 2024Large Language Model Enhanced Recommender Systems: A Surveycited by 10
  24. 2025Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detectioncited by 10
  25. 2025Interactive Recommendation Agent with Active User Commandscited by 10
  26. 2024Persona Setting Pitfall: Persistent Outgroup Biases in Large Language Models Arising from Social Identity Adoptioncited by 9
  27. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  28. 2024Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenizationcited by 9
  29. 2025Improving LLM-powered Recommendations with Personalized Informationcited by 9
  30. 2025LLM-based User Profile Management for Recommender Systemcited by 9
  31. 2025ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Modelscited by 9
  32. 2025Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systemscited by 9
  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. 2024Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendationcited by 8
  36. 2024An Automatic Graph Construction Framework based on Large Language Models for Recommendationcited by 8
  37. 2025Diffusion Generative Recommendation with Continuous Tokenscited by 8
  38. 2025CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Languagecited by 8

50 most cited of 171 All 171 (JSON)

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  1. 2024Large Language Model with Graph Convolution for Recommendation
  2. 2025Training Large Recommendation Models via Graph-Language Token Alignment
  3. 2024Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
  4. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  5. 2024LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
  6. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations
  7. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  8. 2024Large Language Models Enhanced Collaborative Filtering