MATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?

• URL: http://arxiv.org/abs/2504.20094v1
• Date: Sat, 26 Apr 2025 00:55:43 GMT
• Title: MATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?
• Authors: Zheng Hui, Xiaokai Wei, Yexi Jiang, Kevin Gao, Chen Wang, Frank Ong, Se-eun Yoon, Rachit Pareek, Michelle Gong,
• Abstract summary: We propose a multi-agent collaboration framework called MATCHA for conversational recommendation system.<n>Users can request recommendations via free-form text and receive curated lists aligned with their interests, preferences, and constraints.<n>Our system introduces specialized agents for intent analysis, candidate generation, ranking, re-ranking, explainability, and safeguards.
• Score: 8.392396674816638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a multi-agent collaboration framework called MATCHA for conversational recommendation system, leveraging large language models (LLMs) to enhance personalization and user engagement. Users can request recommendations via free-form text and receive curated lists aligned with their interests, preferences, and constraints. Our system introduces specialized agents for intent analysis, candidate generation, ranking, re-ranking, explainability, and safeguards. These agents collaboratively improve recommendations accuracy, diversity, and safety. On eight metrics, our model achieves superior or comparable performance to the current state-of-the-art. Through comparisons with six baseline models, our approach addresses key challenges in conversational recommendation systems for game recommendations, including: (1) handling complex, user-specific requests, (2) enhancing personalization through multi-agent collaboration, (3) empirical evaluation and deployment, and (4) ensuring safe and trustworthy interactions.

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