Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

翻译：推荐系统旨在为用户提供相关建议，但通常缺乏可解释性，且难以捕捉用户行为与属性之间的高层次语义关联。本文提出了一种创新方法，利用大语言模型（LLMs）构建个性化推理图。这些图通过因果与逻辑推理将用户属性与行为序列相连接，以可解释的方式表征用户兴趣。我们的方法——大语言模型推理图（LLMRG）包含四个组件：链式图推理、发散扩展、自验证与评分以及知识库自优化。生成的推理图通过图神经网络编码，作为额外输入以改进传统推荐系统，无需额外用户或物品信息。该方法展示了LLMs如何通过个性化推理图实现更具逻辑性与可解释性的推荐系统。LLMRG使推荐既能受益于工程化的推荐系统，又能融合LLM衍生的推理图。我们在基准测试和实际场景中验证了LLMRG在增强基础推荐模型方面的有效性。