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 human-machine interaction interface to test and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.

中文翻译：

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CRSLab：用于构建会话推荐系统的开源工具包

近年来，会话推荐系统（CRS）在研究界引起了很多关注。但是，现有的CRS研究在场景，目标和技术方面各不相同，缺乏统一，标准化的实施或比较方法。为了应对这一挑战，我们提出了一个开源CRS工具包CRSLab，该工具包提供了一个统一且可扩展​​的框架，其中包含高度分离的模块来开发CRS。基于此框架，我们收集了6个常用的带有人类注释的CRS数据集，并实现了18个模型，其中包括诸如图神经网络和预训练模型之类的最新技术。此外，我们的工具包还提供了一系列自动评估协议和人机交互界面，以测试和比较不同的CRS方法。该项目和文档在https://github.com/RUCAIBox/CRSLab发布。