Towards Topic-Guided Conversational Recommender System
Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, Ji-Rong Wen · 2020
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations.To develop an effective CRS, the support of high-quality datasets is essential.Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario.In this paper, we contribute a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog).Our dataset has two major features.First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario.Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable.Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task.Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation.TG-ReDial is available at https