论文标题
致力于主题引导的会话推荐系统
Towards Topic-Guided Conversational Recommender System
论文作者
论文摘要
会话推荐系统（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CR，必须支持高质量数据集。现有的CRS数据集主要关注用户的即时请求，而对建议方案缺乏积极的指导。在本文中，我们通过\ textbf {t} opic- \ textbf {g textbf {g} u textbf {u textbf {uttextbf {dial} og）贡献了一个名为\ textbf {tg-redial}（\ textbf {re}称赞的新的CRS数据集（\ textbf {re}。我们的数据集具有两个主要功能。首先，它结合了主题线程，以实施自然语义过渡到建议方案。其次，它是以半自动方式创建的，因此人类注释更合理和可控。基于TG-REDIAL，我们介绍了主题指导的会话建议的任务，并提出了有效的方法来解决此任务。广泛的实验证明了我们方法对三个子任务的有效性，即主题预测，项目建议和响应产生。 TG-REDIAL可从https://github.com/rucaibox/tg-redial获得。
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.
