G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation

• URL: http://arxiv.org/abs/2502.12586v1
• Date: Tue, 18 Feb 2025 06:42:38 GMT
• Title: G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
• Authors: Yuhan Li, Xinni Zhang, Linhao Luo, Heng Chang, Yuxiang Ren, Irwin King, Jia Li,
• Abstract summary: We propose a framework using graph retrieval-augmented large language models (LLMs) for explainable recommendation.<n>G-Refer achieves superior performance compared with existing methods in both explainability and stability.
• Score: 48.23263809469786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide personalized and interpretable explanations, existing works often combine the generation capabilities of large language models (LLMs) with collaborative filtering (CF) information. CF information extracted from the user-item interaction graph captures the user behaviors and preferences, which is crucial for providing informative explanations. However, due to the complexity of graph structure, effectively extracting the CF information from graphs still remains a challenge. Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations. To address these challenges, we propose G-Refer, a framework using graph retrieval-augmented large language models (LLMs) for explainable recommendation. Specifically, we first employ a hybrid graph retrieval mechanism to retrieve explicit CF signals from both structural and semantic perspectives. The retrieved CF information is explicitly formulated as human-understandable text by the proposed graph translation and accounts for the explanations generated by LLMs. To bridge the modality gap, we introduce knowledge pruning and retrieval-augmented fine-tuning to enhance the ability of LLMs to process and utilize the retrieved CF information to generate explanations. Extensive experiments show that G-Refer achieves superior performance compared with existing methods in both explainability and stability. Codes and data are available at https://github.com/Yuhan1i/G-Refer.

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