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

TL;DR

CRSLab is an open-source, modular toolkit that standardizes development, evaluation, and comparison of conversational recommender systems using multiple datasets and models.

Contribution

It provides a unified, extensible framework with diverse datasets, models, and evaluation tools for CRS research and development.

Findings

Implemented 18 models including recent techniques

Collected 6 human-annotated CRS datasets

Enabled standardized testing and comparison

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.