Segment, Recommend, and Explain: Advancing Conversational Recommender Systems with Large Language Model Agents

From personalized shopping assistants to streaming service recommendations, Conversational Recommender Systems (CRSs) have become essential tools for decision-making.However, users often struggle to understand why particular items are recommended, leading to reduced trust, lower engagement, and less effective decisionmaking.At the same time, CRSs usually face challenges in recommendation accuracy as they struggle to integrate historical data, real-time feedback, and contextual signals effectively.This combination of unclear explanations and suboptimal recommendations diminishes user experience and system reliability.To address these challenges, we explore three key questions: (i) how the integration of structured and unstructured data can enhance downstream tasks in a Multi-Agent System (MAS)-based CRS architecture, (ii) how a MAS can generate dynamic and user-centric explanations, and (iii) how cross-agent collaboration can optimize recommendation accuracy.MAS-based architectures distribute critical tasks such as decision-making, data retrieval, optimization, and reasoning across specialized agents.A meta-agent oversees these agents, ensuring coordination and adaptability.This research aims to enhance the clarity of explanations and improve recommendation accuracy by integrating modular agents in a coordinated MAS framework, providing more personalized explanations and adapting to evolving user preferences.