Related papers: Towards Topic-Guided Conversational Recommender System

Towards Topic-Guided Conversational Recommender System

URL: http://arxiv.org/abs/2010.04125v2
Date: Mon, 2 Nov 2020 14:25:58 GMT
Title: Towards Topic-Guided Conversational Recommender System
Authors: Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang and Ji-Rong Wen
Abstract summary: We contribute a new CRS dataset named textbfTG-ReDial (textbfRecommendation through textbfTopic-textbfGuided textbfDialog) We present the task of topic-guided conversational recommendation, and propose an effective approach to this task.
Score: 80.3725246715938
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 \textbf{TG-ReDial} (\textbf{Re}commendation through \textbf{T}opic-\textbf{G}uided \textbf{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. TG-ReDial is available at https://github.com/RUCAIBox/TG-ReDial.

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