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.

翻译：近年来，知识图谱作为物品侧辅助信息被整合到推荐系统中，以提高推荐准确性。然而，由于用户侧特征粒度不当和固有的稀疏性，构建并整合结构化的用户侧知识仍然是一个重大挑战。大型语言模型（LLMs）的最新进展，凭借其对人类行为的理解和丰富的现实世界知识，为弥合这一差距提供了可能。然而，将LLM生成的信息整合到推荐系统中仍面临挑战，包括噪声信息风险以及额外知识迁移的需求。本文提出了一种基于LLM的用户侧知识推理方法，并设计了一个精心构建的推荐框架以应对这些挑战。我们的方法利用LLM基于用户历史行为推断其兴趣，并将此用户侧信息与物品侧数据及协同数据相结合，构建出一种混合结构：协同兴趣知识图谱（CIKG）。此外，我们提出了一个基于CIKG的推荐框架，该框架包含用户兴趣重建模块和跨域对比学习模块，以减轻潜在噪声并促进知识迁移。我们在三个真实世界数据集上进行了大量实验，以验证我们方法的有效性。与竞争基线相比，我们的方法实现了最先进的性能，尤其对于交互稀疏的用户表现更为突出。