Topic-Guided Conversational Recommender in Multiple Domains

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.

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 provide 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 recommendation 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.

Discussion / Conclusion. We introduced a high-quality dataset TG-ReDial for conversational recommender systems, which was constructed by human annotation based on real-world user data. Based on TG-ReDial, we presented the task of topic-guided conversational recommendation and a solution to this task. Extensive experiments have demonstrated the effectiveness of the proposed approach on three sub-tasks. Currently, the potential of TG-ReDial dataset has not been fully explored. It can be useful as a testbed for more tasks, such as personalized chit-chat (Zhang et al., 2018a), target-guided conversation (Tang et al., 2019) and sequential recommendation (Zhou et al., 2020a). As future work, we will investigate the study of these tasks on TG-ReDial dataset. Besides, we will also consider how to construct more effective approaches to topic-guided conversational recommendation.