UC San Diego

Recommender systems are fundamental to numerous personalization services. However, traditional systems often lack the ability to proactively explore user interests, explain their choices, or adapt immediately to user feedback, leaving users unsatisfied with the recommendation results. Conversational recommender systems, instead, offer a promising future with the capability of understanding both explicit textual feedback and implicit behavior patterns. Such systems accurately model user sequential interactions, proactively engage with users to explore preferences and provide explanations and context for their recommendations through conversations. The research on conversational recommender systems marks a crucial step in the evolution from traditional recommender systems to truly personalized intelligent agents.

In this dissertation, our research focuses on three pillars in developing effective conversational recommender systems: (1) Accuracy: modeling the dynamic and evolving nature of user preferences within sequential behaviors to ensure recommendations better match the user’s needs. (2) Explainability: improving the quality and expressiveness of explanations that accompany recommendations to enhance user understanding and trust. (3) Interactivity: exploring multi-round conversational capabilities to support complex recommendation scenarios, including managing bundled item recommendations and seamlessly integrating with Large Language Models for a natural-language-driven user experience.By advancing conversational recommender systems with the components for accuracy, explainability, and interactivity, this research paves the way for future research to create powerful conversational recommender systems that reshape how users discover and interact with products and services.