mlsanityData to 14 Oct 2026

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

arXiv 2306.10933 ↗First posted 19 Jun 2023cs.IRcited by 74

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Abstract (arXiv)

Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to open-world knowledge. Recently, the emergence of large language models (LLMs) has shown promise in bridging this gap by encoding extensive world knowledge and demonstrating reasoning capability. Nevertheless, previous attempts to directly use LLMs as recommenders have not achieved satisfactory results. In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items. We introduce factorization prompting to elicit accurate reasoning on user preferences. The generated reasoning and factual knowledge are effectively transformed and condensed into augmented vectors by a hybrid-expert adaptor in order to be compatible with the recommendation task. The obtained vectors can then be directly used to enhance the performance of any recommendation model. We also ensure efficient inference by preprocessing and prestoring the knowledge from the LLM. Extensive experiments show that KAR significantly outperforms the state-of-the-art baselines and is compatible with a wide range of recommendation algorithms. We deploy KAR to Huawei's news and music recommendation platforms and gain a 7% and 1.7% improvement in the online A/B test, respectively.

Key facts. Cited by 74 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jun 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 17; 2025: 32; 2026: 16.

74 papers.

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  1. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  2. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 284
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  6. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  7. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  8. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  9. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  10. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  11. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  12. 2025A Survey of Personalization: From RAG to Agentcited by 27
38 more
  1. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  2. 2025LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendationcited by 19
  3. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  4. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  5. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  6. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulationcited by 16
  7. 2025InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generationcited by 15
  8. 2024ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoningcited by 14
  9. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 12
  10. 2025Position: The Real Barrier to LLM Agent Usability is Agentic ROIcited by 12
  11. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  12. 2023MAP: A Model-agnostic Pretraining Framework for Click-through Rate Predictioncited by 10
  13. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  14. 2025TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendationcited by 7
  15. 2025Masked Diffusion for Generative Recommendationcited by 7
  16. 2025Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Modelcited by 6
  17. 2025NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendationscited by 6
  18. 2024Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loopcited by 5
  19. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5
  20. 2025DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systemscited by 5
  21. 2025Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Trainingcited by 4
  22. 2025Field Matters: A Lightweight LLM-enhanced Method for CTR Predictioncited by 4
  23. 2025CTR-Sink: Attention Sink for Language Models in Click-Through Rate Predictioncited by 4
  24. 2024Large Language Models Make Sample-Efficient Recommender Systemscited by 3
  25. 2024Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Predictioncited by 3
  26. 2025Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographicscited by 3
  27. 2026CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendationscited by 3
  28. 2024A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environmentscited by 2
  29. 2024LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filteringcited by 2
  30. 2025Large Language Models Enhanced Hyperbolic Space Recommender Systemscited by 2
  31. 2025Generative Representational Learning of Foundation Models for Recommendationcited by 2
  32. 2025Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendationscited by 2
  33. 2026Verifiable Reasoning for LLM-based Generative Recommendationcited by 2
  34. 2024Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generationcited by 1
  35. 2024Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systemscited by 1
  36. 2025Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intentcited by 1
  37. 2025LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosiscited by 1
  38. 2025Serendipitous Recommendation with Multimodal LLMcited by 1

50 most cited of 74 All 74 (JSON)

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  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Survey
  2. 2024Aligning Large Language Models with Recommendation Knowledge
  3. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  4. 2025KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation
  5. 2024Large Language Models Enhanced Collaborative Filtering
  6. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  7. 2026Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
  8. 2023Leveraging Large Language Models for Pre-trained Recommender Systems