Towards Topic-Guided Conversational Recommender System (2020)

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 ( Re commendation through T opic- G uided 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 blue https://github.com/RUCAIBox/TG-ReDial .

## introduction recently, conversational recommender system (crs) @xcite @xcite @xcite 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 @xcite @xcite and chit-chat based goal completion @xcite @xcite . 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 @xcite . this issue has been explored to some extent by durecdial dataset @xcite . 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 questionanswering). 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 @xcite 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 the characteristics (e.g., interest) of annotators or predefined identities. to tackle the above problems, we construct a new crs dataset named recommendation through topic-guided dialog (tg-redial). it consists of 10,000 two-party dialogues between a seeker and a ## dataset construction following @xcite , we consider a two-party setting for conversational recommendation, in which the person and the chat-bot play the roles of the seeker and the recommender, respectively. we expect that the dialogue between the seeker and the recommender starts from a nonrecommendation scenario. the recommender proactively guides the conversation into the target topic, and then makes suitable recommendation considering the seeker's interests. ## 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 @xcite , target-guided conversation @xcite and sequential recommendation @xcite . 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.