MATRAG：多智能体透明检索增强生成用于可解释推荐
MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations
AI总结：
MATRAG通过多智能体协作与知识图谱增强检索，提升推荐系统的透明性和可解释性，实验表明其在准确率和可信度上均优于现有方法。
摘要：
基于大型语言模型（LLM）的推荐系统在理解用户偏好和生成个性化建议方面表现出色，但存在透明性、知识支撑和解释一致性等挑战。本文提出MATRAG框架，结合多智能体协作与知识图谱增强检索，通过四个专用智能体（用户建模、物品分析、推理和解释）生成可解释推荐。框架包含透明度评分机制，评估解释的可信度和相关性。在Amazon Reviews、MovieLens-1M和Yelp三个基准数据集上的实验表明，MATRAG在命中率和NDCG上分别提升12.7%和15.3%，且87.4%的生成解释被领域专家评为有用和可信。
英文摘要：
Large Language Model (LLM)-based recommendation systems have demonstrated remarkable capabilities in understanding user preferences and generating personalized suggestions. However, existing approaches face critical challenges in transparency, knowledge grounding, and the ability to provide coherent explanations that foster user trust. We introduce MATRAG (Multi-Agent Transparent Retrieval-Augmented Generation), a novel framework that combined multi-agent collaboration with knowledge graph-augmented retrieval to deliver explainable recommendations. MATRAG employs four specialized agents: a User Modeling Agent that constructs dynamic preference profiles, an Item Analysis Agent that extracts semantic features from knowledge graphs, a Reasoning Agent that synthesizes collaborative and content-based signals, and an Explanation Agent that generates natural language justifications grounded in retrieved knowledge. Our framework incorporates a transparency scoring mechanism that quantifies explanation faithfulness and relevance. Extensive experiments on three benchmark datasets (Amazon Reviews, MovieLens-1M, and Yelp) demonstrate that MATRAG achieves state-of-the-art performance, improving recommendation accuracy by 12.7\% (Hit Rate) and 15.3\% (NDCG) over leading baselines, while human evaluation confirms that 87.4\% of generated explanations are rated as helpful and trustworthy by domain experts. Our work establishes new benchmarks for transparent, agentic recommendation systems and provides actionable insights for deploying LLM-based recommenders in production environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
