Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation

TL;DR

This paper introduces K-RagRec, a retrieval-augmented framework that enhances LLM-based recommender systems by incorporating structured knowledge from knowledge graphs to improve recommendation quality.

Contribution

The paper proposes a novel retrieval-augmented method that leverages knowledge graph structure to address limitations of existing LLM-based recommenders.

Findings

K-RagRec improves recommendation accuracy over baseline models.

Incorporating structured KG information reduces hallucinations in LLM recommendations.

Experiments demonstrate the effectiveness of the proposed retrieval framework.

Abstract

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.