Research Area:  Machine Learning

Abstract:

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 extbf{TG-ReDial} ( extbf{Re}commendation through extbf{T}opic- extbf{G}uided extbf{Dial}og). 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.

Keywords:  
Computation and Language
Human-Computer Interaction
Information Retrieval
Conversational Recommender Systems
TG-ReDial

Author(s) Name:  Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, Ji-Rong Wen

Publisher name:   arXiv:2010.04125

DOI:  10.48550/arXiv.2010.04125

Volume Information:  

Paper Link:   https://arxiv.org/abs/2010.04125