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.

翻译：兼容推荐系统( CRS) 旨在通过交互式对话向用户推荐高质量的项目。 为了开发有效的 CRS, 支持高质量的数据集至关重要。 现有的 CRS 数据集主要侧重于用户的即时请求, 但却缺乏对建议设想方案的积极指导。 在本文中, 我们提供了一个新的 CRS 数据集, 名为\ textbff{ TG- ReDal} (\ textbf{T}} 校对建议 ) (\ textb{ opic- textbf{G}uided\ textbf{Dial}og ) 。 我们的数据集有两个主要特征。 首先, 它包含主题线索, 以强制自然向建议设想方案过渡。 其次, 它以半自动方式创建, 因而人类的注意更加合理和可控性。 基于 TG- ReDalal, 我们介绍了专题引导的对话建议任务, 并提出了对这项任务的有效方法。 广泛的实验展示了我们在三个子目录上的方法的有效性, 即专题预测、 REG 项/ RUG 的生成。 。 TRAG 是 。 ATG 。 。