Towards Topic-Guided Conversational Recommender System

Proceedings of the 28th International Conference on Computational Linguistics, pages 4128–4139 Barcelona, Spain (Online), December 8-13, 2020 4128 Towards Topic-Guided Conversational Recommender System Kun Zhou1†, Yuanhang Zhou1†, Wayne Xin Zhao2,3∗, Xiaoke Wang1 and Ji-Rong Wen2,3 1School of Information, Renmin University of China 2Gaoling School of Artificial Intelligence, Renmin University of China 3Beijing Key Laboratory of Big Data Management and Analysis Methods francis kun zhou@163.com, {sdzyh002,batmanfly}@gmail.com {yarkonawang,jrwen}@ruc.edu.cn, 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 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://github.com/RUCAIBox/TG-ReDial. 1 Introduction Recently, conversational recommender system (CRS) (Chen et al., 2019; Sun and Zhang, 2018; Li et al., 2018; Zhang et al., 2018b; Liao et al., 2019) has become an emerging research topic, which aims to pro- vide high-quality recommendations to users through natural language conversations. Generally, a CRS is composed of a recommender component and a dialog component, which make suitable recommen- dation and generate proper response, respectively. To develop an effective CRS, high-quality datasets are crucial to learn the model parameters. Existing CRS datasets roughly fall into two main categories, namely attribute-based user simulation (Sun and Zhang, 2018; Lei et al., 2020; Zhang et al., 2018b) and chit-chat based goal completion (Li et al., 2018; Chen et al., 2019; Liu et al., 2020). These datasets usually assume that a user has clear, immediate requests when interacting with the system. They lack the proactive guidance (or transitions) from non-recommendation scenarios to the desired recommendation scenario. Indeed, it has become increasingly important that recommendations can be naturally triggered according to conversation context (Tang et al., 2019; Kang et al., 2019). This issue has been explored to some extent by DuRecDial dataset (Liu et al., 2020). DuRecDial has characterized the goal-planning process by constructing a goal sequence. However, it mainly focuses on type switch or coverage for dialog sub-tasks (e.g., non-recommendation, recommendation and question- answering). Explicit semantic transition that leads up to the recommendation has not been well studied or discussed in DuRecDial dataset. Besides, most of existing CRS datasets (Liu et al., 2020; Li et al., 2018) mainly rely on human annotators to create user profiles or generate the conversations. It is difficult to capture rich, complicated cases from real-world applications with a limited number of human annotators, since the generated conversations mainly reflect …

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