Reviewer assignment algorithms for peer review automation: A survey

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摘要

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.

论文关键词:Matching degree,Information retrieval,Reviewer assignment problem,Optimization algorithm,Natural language processing,Peer review

论文评审过程:Received 9 March 2022, Revised 4 July 2022, Accepted 11 July 2022, Available online 3 August 2022, Version of Record 3 August 2022.

论文官网地址:https://doi.org/10.1016/j.ipm.2022.103028