mlsanityData to 14 Oct 2026

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv 2506.07449 ↗First posted 9 Jun 2025cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG for recommendation

Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG approaches predominantly rely on flat, similarity-based retrieval that fails to leverage the rich relational structure inherent in user-item interactions. We introduce LlamaRec-LKG-RAG, a novel single-pass, end-to-end trainable framework that integrates personalized knowledge graph context into LLM-based recommendation ranking. Our approach extends the LlamaRec architecture by incorporating a lightweight user preference module that identifies salient relation paths within a heterogeneous knowledge graph constructed from user behavior and item metadata. These personalized subgraphs are seamlessly integrated into prompts for a fine-tuned Llama-2 model, enabling efficient and interpretable recommendations through a unified inference step. Comprehensive experiments on ML-100K and Amazon Beauty datasets demonstrate consistent improvements over LlamaRec across key ranking metrics (MRR, NDCG, Recall). LlamaRec-LKG-RAG demonstrates the critical value of structured reasoning in LLM-based recommendations and establishes a foundation for scalable, knowledge-aware personalization in next-generation recommender systems. Code is available at~hrefhttps://github.com/VahidAz/LlamaRec-LKG-RAGrepository.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 39 references that are in the map. First posted on arXiv 9 Jun 2025.

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29 of 39 references.

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  1. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation4 Jan

Other references · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2016Layer Normalizationcited by 5,669
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  6. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  7. 2023A Survey of Large Language Modelscited by 2,576
  8. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  9. 2015Image-based Recommendations on Styles and Substitutescited by 706
  10. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  11. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  12. 2023A Survey on Large Language Models for Recommendationcited by 379
16 more
  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  2. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  5. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  6. 2024GRAG: Graph Retrieval-Augmented Generationcited by 78
  7. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  8. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generationcited by 64
  9. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  10. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 50
  11. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  12. 2023Linear Recurrent Units for Sequential Recommendationcited by 32
  13. 2024On Explaining Recommendations with Large Language Models: A Reviewcited by 15
  14. 2023Large Language Models are Not Stable Recommender Systemscited by 13
  15. 2022Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenderscited by 11
  16. 2025RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphscited by 2

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  5. 2024LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
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  7. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  8. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendation