MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations

arXiv cs.AI / 4/25/2026

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Key Points

The paper introduces MATRAG, a multi-agent framework that enhances LLM-based recommender systems by adding transparent, knowledge-grounded explanation generation.
MATRAG uses four specialized agents—user modeling, item feature extraction from knowledge graphs, reasoning over signals, and an explanation agent that produces natural-language justifications based on retrieved knowledge.
It introduces a transparency scoring mechanism to measure how faithful and relevant the generated explanations are to the underlying retrieved information.
Experiments on Amazon Reviews, MovieLens-1M, and Yelp show state-of-the-art results, improving Hit Rate by 12.7% and NDCG by 15.3% versus strong baselines.
Human evaluation indicates that 87.4% of the generated explanations are considered helpful and trustworthy by domain experts, supporting the framework’s explainability goals.

Abstract

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.