Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 430-436.DOI: 10.11772/j.issn.1001-9081.2021122127

• Data science and technology • Previous Articles    

Reviewer recommendation algorithm based on affinity and research direction coverage

Lei ZHONG1, Yunsheng ZHOU1, Dunhui YU1,2, Haibo CUI1,2()   

  1. 1.School of Computer Science and Information Engineering,Hubei University,Wuhan Hubei 430062,China
    2.Hubei Engineering Technology Research Center of Educational Informatization (Hubei University),Wuhan Hubei 430062,China
  • Received:2021-12-27 Revised:2022-03-18 Accepted:2022-06-20 Online:2022-08-03 Published:2023-02-10
  • Contact: Haibo CUI
  • About author:ZHONG Lei, born in 2001. His research interests include natural language processing, knowledge graph.
    ZHOU Yunsheng, born in 2002. His research interests include knowledge graph.
    YU Dunhui, born in 1974, Ph. D., professor. His research interests include knowledge graph, swarm intelligence.
  • Supported by:
    National Natural Science Foundation of China(61977021);Hubei Province Technological Innovation Special Project (Major Project)(2020AEA008)


钟磊1, 周允升1, 余敦辉1,2, 崔海波1,2()   

  1. 1.湖北大学 计算机与信息工程学院,武汉 430062
    2.湖北省教育信息化工程技术研究中心(湖北大学),武汉 430062
  • 通讯作者: 崔海波
  • 作者简介:钟磊(2001—),男,湖北孝感人,主要研究方向:自然语言处理、知识图谱
  • 基金资助:


To deal with the problem that the existing reviewer recommendation algorithms assign reviewers only through affinity score and ignore the research direction matching between reviewers and papers to be reviewed, a reviewer recommendation algorithm based on Affinity and Research Direction Coverage (ARDC) was proposed. Firstly, the order of the paper’s selection of reviewers was determined according to the frequencies of the research directions appearing in the papers and the reviewer’s paper groups. Secondly, the reviewer’s comprehensive review score to the paper to be reviewed was calculated based on the affinity score between the reviewers and the paper to be reviewed and the research direction coverage score of the reviewers to the paper to be reviewed, and the pre-assigned review team for the paper was obtained on the basis of round-robin scheduling. Finally, the final recommendation of the review team was realized based on the conflict of interest conflict inspection and resolution. Experimental results show that compared with assignment based reviewer recommendation algorithms such as Fair matching via Iterative Relaxation (FairIR) and Fair and Accurate reviewer assignment in Peer Review (PR4A), the proposed algorithm has the average research direction coverage score increased by 38% on average at the expense of a small amount of affinity score, so that the recommendation result is more accurate and reasonable.

Key words: reviewer recommendation, affinity score, topic model, interest conflict, round-robin scheduling



关键词: 审稿人推荐, 亲和力分数, 主题模型, 利益冲突, 轮询调度

CLC Number: