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Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation

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arxiv 2501.02226 v2 pith:7YTJ56JA submitted 2025-01-04 cs.IR

Shijie Wang , Wenqi Fan , Yue Feng , Shanru Lin , Xinyu Ma , Shuaiqiang Wang , Dawei Yin This is my paper
classification cs.IR
keywords knowledgegenerationinformationlimitationsllm-basedrecommenderretrieval-augmentedsystems
verification ladder T0 review T1 audit T2 compute T3 formal
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Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The emergence of Large Language Models (LLMs) has yielded remarkable achievements, demonstrating their potential for the development of next-generation recommender systems. Despite these advancements, LLM-based recommender systems face inherent limitations stemming from their LLM backbones, particularly issues of hallucinations and the lack of up-to-date and domain-specific knowledge. Recently, Retrieval-Augmented Generation (RAG) has garnered significant attention for addressing these limitations by leveraging external knowledge sources to enhance the understanding and generation of LLMs. However, vanilla RAG methods often introduce noise and neglect structural relationships in knowledge, limiting their effectiveness in LLM-based recommendations. To address these limitations, we propose to retrieve high-quality and up-to-date structure information from the knowledge graph (KG) to augment recommendations. Specifically, our approach develops a retrieval-augmented framework, termed K-RagRec, that facilitates the recommendation generation process by incorporating structure information from the external KG. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed method.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA

    cs.CV 2025-08 unverdicted novelty 7.0 of 10

    mKG-RAG constructs multimodal KGs via MLLM-driven extraction and vision-text matching then applies dual-stage query-aware retrieval to achieve new state-of-the-art results on knowledge-based VQA.

  2. GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation

    cs.IR 2026-08 conditional novelty 6.0 of 10

    GARDRec improves LLM-based next-item ranking by grounding decisions in knowledge-graph embeddings, personalized graph contexts, and late-stage scoring rather than prompt text.

  3. Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges

    cs.IR 2026-05 unverdicted novelty 6.0 of 10

    A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.

  4. ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning

    cs.IR 2026-04 unverdicted novelty 6.0 of 10

    ReRec uses reinforcement fine-tuning with dual-graph reward shaping, reasoning-aware advantage estimation, and online curriculum scheduling to improve LLM reasoning and performance in recommendation tasks.

  5. Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders

    cs.IR 2026-04 unverdicted novelty 6.0 of 10

    KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.

  6. Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

    cs.CL 2025-09 unverdicted novelty 6.0 of 10

    KG-R1 trains a single RL agent to retrieve from and reason over knowledge graphs in one loop, achieving higher accuracy with fewer tokens than multi-module baselines and transferring to unseen graphs.

  7. Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs

    cs.CL 2025-08 conditional novelty 5.0 of 10

    RevBrowse retrieves preference-relevant pros and cons from reviews via a contrastively trained module, then uses an LLM to rerank candidates; experiments on four Amazon datasets show consistent improvements over baselines.

  8. X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking

    cs.IR 2026-08 conditional novelty 4.0 of 10

    X-KGRank couples a LightGCN recommender with knowledge-graph-grounded LLM explanations, reporting +17% NDCG@10 over a popularity baseline on MovieLens-1M, though the LLM does not affect the final ranking.

  9. Effectiveness of LLMs in Temporal User Profiling for Recommendation

    cs.IR 2025-10 conditional novelty 4.0 of 10

    LLM-generated temporal user profiles, fused by attention, improve content-based recommendation in high-activity domains but yield mixed results in sparse domains.

  10. MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

    cs.AI 2025-08 reject novelty 4.0 of 10

    The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.

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