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

• URL: http://arxiv.org/abs/2101.00939v1
• Date: Mon, 4 Jan 2021 13:10:31 GMT
• Title: CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
• Authors: Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen
• Abstract summary: conversational recommender system (CRS) has received much attention in the research community. Existing studies on CRS vary in scenarios, goals and techniques, lacking unified, standardized implementation or comparison. We propose an open-source CRS toolkit CRSLab, which provides a unified framework with highly-decoupled modules to develop CRSs.
• Score: 57.208266345350474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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