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Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation
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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.
Forward citations
Cited by 7 Pith papers
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GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
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.
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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
A new benchmark (TruthHypo) and a knowledge-grounded hallucination detector (KnowHD) show that grounding scores can partially select truthful LLM-generated biomedical hypotheses, but the result is at risk from knowled...
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Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs
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.
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X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking
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.
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Effectiveness of LLMs in Temporal User Profiling for Recommendation
LLM-generated temporal user profiles, fused by attention, improve content-based recommendation in high-activity domains but yield mixed results in sparse domains.
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MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems
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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LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
A KG-enhanced LlamaRec that feeds user-specific relation paths into a Llama-2 ranker reports modest MRR, NDCG, and Recall gains on two benchmarks.
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