Conversational recommender systems (CRS) promise to provide a more natural user experience for exploring and discovering items of interest through ongoing conversation. However, effectively modeling user preferences during conversations and generating personalized recommendations in real time remain challenging problems. Users often express their needs in a vague and evolving manner, and CRS must adapt to capture the dynamics and uncertainty in user preferences to have productive interactions.

This research develops user-centric methods for building conversational recommendation system that can understand complex and changing user needs. We propose a graph-based conversational recommendation framework that represents multi-turn conversations as reasoning over a user-item-attribute graph. Enhanced conversational path reasoning incorporates graph neural networks to improve representation learning in this framework. To address uncertainty and dynamics in user preferences, we present the vague preference multi-round conversational recommendation scenario and an adaptive vague preference policy learning solution that employs reinforcement learning to determine recommendation and preference elicitation strategies tailored to the user.

Looking to the future, large language models offer promising opportunities to enhance various aspects of CRS, including user modeling, policy learning, response generation. Overall, this research takes a user-centered perspective in designing conversational agents that can adapt to the inherent ambiguity involved in natural language dialogues with people.

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