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

TL;DR

MATRAG is a multi-agent framework that combines knowledge graph retrieval and explanation generation to improve transparency and trustworthiness in recommendation systems, achieving state-of-the-art accuracy and helpful explanations.

Contribution

The paper introduces MATRAG, a novel multi-agent system integrating knowledge graphs and explanation mechanisms for transparent, accurate recommendations.

Findings

Achieves 12.7% improvement in Hit Rate over baselines.

Attains 15.3% higher NDCG compared to existing methods.

87.4% of explanations rated as helpful by domain experts.

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.