Variational Reasoning about User Preferences for Conversational Recommendation
Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Zhi Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
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.