Abstract
Recent attempts to integrate large language models (LLMs) into recommender systems have gained momentum, but most remain limited to simple text generation or static prompt-based inference, failing to capture the complexity of user preferences and real-world interactions. This study proposes the Multi-Aspect Driven LLM Agent MADRec, an autonomous LLM-based recommender that constructs user and item profiles by unsupervised extraction of multi-aspect information from reviews and performs direct recommendation, sequential recommendation, and explanation generation. MADRec generates structured profiles via aspect-category-based summarization and applies Re-Ranking to construct high-density inputs. When the ground-truth item is missing from the output, the Self-Feedback mechanism dynamically adjusts the inference criteria. Experiments across multiple domains show that MADRec outperforms traditional and LLM-based baselines in both precision and explainability, with human evaluation further confirming the persuasiveness of the generated explanations.
Abstract (translated)
近期将大型语言模型（LLMs）集成到推荐系统中的尝试逐渐增多，但大多数仍局限于简单的文本生成或静态提示推理，未能捕捉用户偏好和现实世界互动的复杂性。本研究提出了一个多方面驱动的LLM代理——MADRec（Multi-Aspect Driven LLM Agent），这是一种自主的基于LLM的推荐器。它通过无监督地从评论中提取多方面的信息来构建用户和物品档案，并执行直接推荐、顺序推荐和解释生成。 MADRec利用以方面类别为基础的摘要生成结构化档案，并应用重排序技术构造高密度输入。当真实目标项目缺失在输出结果时，自反馈机制会动态调整推理标准。跨多个领域的实验表明，MADRec在精度和可解释性方面均优于传统方法以及基于LLM的方法基准线，且人类评估进一步确认了生成解释的说服力。
URL
https://arxiv.org/abs/2510.13371
