首页|A multilayer network diffusion-based model for reviewer recommendation

A multilayer network diffusion-based model for reviewer recommendation

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With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential review-ers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62％in recall,5.66％in hit rate,and 47.53％in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

reviewer recommendationmultilayer networknetwork diffusion modelrecommender systemscomplex networks

黄羿炜、徐舒琪、蔡世民、吕琳媛

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Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 611731,China

Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou 313001,China

Institute of Dataspace,Hefei Comprehensive National Science Center,Hefei 230088,China

Big Data Research Center,University of Electronic Science and Technology of China,Chengdu 611731,China

School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

School of Cyber Science and Technology,University of Science and Technology of China,Hefei 230026,China

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国家自然科学基金New Cornerstone Science Foundation through the XPLORER PRIZE

T2293771

2024

10.1088/1674-1056/ad181d

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(3)
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