Variational Reasoning about User Preferences for Conversational Recommendation

Abstract

Conversational recommender systems (CRSs) provide recommendations through interactive conversations. CRSs typically provide recommendations through relatively straightforward interactions, where the system continuously inquires about a user's explicit attribute-aware preferences and then decides which items to recommend. In addition, topic tracking is often used to provide naturally sounding responses. However, merely tracking topics is not enough to recognize a user's real preferences in a dialogue.