Variational Reasoning about User Preferences for Conversational Recommendation
Zhaochun Ren, Zhi Gang Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen
2022Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval33 citationsDOI
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.
Topics & Concepts
Computer scienceRecommender systemHuman–computer interactionTracking (education)Information retrievalArtificial intelligencePsychologyPedagogyTopic ModelingRecommender Systems and TechniquesSpeech and dialogue systems