MADREC: A Multi-Aspect Driven LLM Agent for Explainable and Adaptive Recommendation

• URL: http://arxiv.org/abs/2510.13371v1
• Date: Wed, 15 Oct 2025 10:03:29 GMT
• Title: MADREC: A Multi-Aspect Driven LLM Agent for Explainable and Adaptive Recommendation
• Authors: Jiin Park, Misuk Kim,
• Abstract summary: Multi-Aspect Driven LLM Agent MADRec is an autonomous recommender that constructs user and item profiles by unsupervised extraction of multi-aspect information from reviews.<n>MADRec generates structured profiles via aspect-category-based summarization and applies Re-Ranking to construct high-density inputs.<n>Experiments across multiple domains show that MADRec outperforms traditional and LLM-based baselines in both precision and explainability.
• Score: 11.430206422495829
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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