Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

• URL: http://arxiv.org/abs/2412.13544v1
• Date: Wed, 18 Dec 2024 06:43:56 GMT
• Title: Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models
• Authors: Zheng Hu, Zhe Li, Ziyun Jiao, Satoshi Nakagawa, Jiawen Deng, Shimin Cai, Tao Zhou, Fuji Ren,
• Abstract summary: Large Language Models (LLMs) offer the potential to bridge the gap by leveraging human behavior understanding and extensive real-world knowledge. We propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.
• Score: 15.41378841915072
• License:
• Abstract: In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.

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