@inproceedings{zhou-etal-2021-crslab,
title = "{CRSL}ab: An Open-Source Toolkit for Building Conversational Recommender System",
author = "Zhou, Kun and
Wang, Xiaolei and
Zhou, Yuanhang and
Shang, Chenzhan and
Cheng, Yuan and
Zhao, Wayne Xin and
Li, Yaliang and
Wen, Ji-Rong",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.22",
doi = "10.18653/v1/2021.acl-demo.22",
pages = "185--193",
abstract = "In recent years, conversational recommender systems (CRSs) have drawn a wide attention in the research community, which focus on providing high-quality recommendations to users via natural language conversations. However, due to diverse scenarios and data formats, existing studies on CRSs lack unified and standardized implementation or comparison. To tackle this challenge, we release an open-source 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 19 models that include advanced techniques such as graph neural networks and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to evaluate and compare different CRS methods. The project and documents are released at \url{https://github.com/RUCAIBox/CRSLab}.",
}
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<abstract>In recent years, conversational recommender systems (CRSs) have drawn a wide attention in the research community, which focus on providing high-quality recommendations to users via natural language conversations. However, due to diverse scenarios and data formats, existing studies on CRSs lack unified and standardized implementation or comparison. To tackle this challenge, we release an open-source 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 19 models that include advanced techniques such as graph neural networks and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to evaluate and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.</abstract>
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%0 Conference Proceedings
%T CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
%A Zhou, Kun
%A Wang, Xiaolei
%A Zhou, Yuanhang
%A Shang, Chenzhan
%A Cheng, Yuan
%A Zhao, Wayne Xin
%A Li, Yaliang
%A Wen, Ji-Rong
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2021-crslab
%X In recent years, conversational recommender systems (CRSs) have drawn a wide attention in the research community, which focus on providing high-quality recommendations to users via natural language conversations. However, due to diverse scenarios and data formats, existing studies on CRSs lack unified and standardized implementation or comparison. To tackle this challenge, we release an open-source 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 19 models that include advanced techniques such as graph neural networks and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to evaluate and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.
%R 10.18653/v1/2021.acl-demo.22
%U https://aclanthology.org/2021.acl-demo.22
%U https://doi.org/10.18653/v1/2021.acl-demo.22
%P 185-193
Markdown (Informal)
[CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://aclanthology.org/2021.acl-demo.22) (Zhou et al., ACL-IJCNLP 2021)
ACL
- Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, and Ji-Rong Wen. 2021. CRSLab: An Open-Source Toolkit for Building Conversational Recommender System. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 185–193, Online. Association for Computational Linguistics.