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

FOS: Computer and information sciences Association (psychology) Trust-Aware Recommender Systems Epistemology 02 engineering and technology Computer Science - Information Retrieval Context-Aware Recommender Systems Engineering Artificial Intelligence 0202 electrical engineering, electronic engineering, information engineering Recommender system Natural Language Processing Computer Science - Computation and Language Architectural engineering Content-Based Recommendation Statistical Machine Translation and Natural Language Processing Open source Computer science Programming language FOS: Philosophy, ethics and religion World Wide Web Philosophy Recommender System Technologies Joint (building) Collaborative Filtering Computer Science Physical Sciences Computation and Language (cs.CL) Software Information Retrieval (cs.IR) Information Systems
DOI: 10.18653/v1/2021.acl-demo.22 Publication Date: 2021-07-27T01:42:51Z
ABSTRACT
8 pages<br/>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.<br/>
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