CRSLab: An Open-Source Toolkit for Building Conversational Recommender System

In recent years, conversational recommender system (CRS) has received much attention in the research community. However, existing studies on CRS vary in scenarios, goals and techniques, lacking unified, standardized implementation or comparison. To tackle this challenge, we propose an open-source CRS toolkit CRSLab, which provides a unified and extensible framework with highly-decoupled modules to develop CRSs. Based on this framework, we collect 6 commonly-used human-annotated CRS datasets and implement 18 models that include recent techniques such as graph neural network and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a humanmachine interaction interface to test and compare different CRS methods. The project and documents are released at https:// github.com/RUCAIBox/CRSLab . Recent years have witnessed remarkable progress in the conversational recommender system (CRS) (Christakopoulou et al., 2016; Sun and Zhang, 2018; Li et al., 2018) , which aims to provide high-quality recommendations to users through natural language conversations. To build an effective CRS, users have proposed a surge of datasets (