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arXiv · 2510.13371

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

Jiin Park·Misuk Kim
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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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Jiin Park, Misuk Kim. 2025-10-15. MADREC: A Multi-Aspect Driven LLM Agent for Explainable and Adaptive Recommendation. https://arxiv.org/abs/2510.13371

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