An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment
Journal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10091109/ |
| _version_ | 1797835855366193152 |
|---|---|
| author | Dong-Hoon Choi Jin Woo Hyun Young Rock Kim |
| author_facet | Dong-Hoon Choi Jin Woo Hyun Young Rock Kim |
| author_sort | Dong-Hoon Choi |
| collection | DOAJ |
| description | Journal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The affinity can be evaluated by whether any of the reviewers has been a coauthor and/or a coworker in a common institution with any of the authors of the manuscript. The expertise depends on the similarity of the research topic between the reviewer’s published papers and the submitted manuscripts. In this paper we propose an algorithm to recommend appropriate reviewers to editors, based on the assessment of these scholarly activities and achievements. To implement this algorithm, TextRank and GenSim library are used to extract feature sets from abstract and introduction sections of both submitted manuscripts and the reviewer candidates’ papers. Based on the extracted feature sets, affinity and expertise check are implemented. An experiment has been conducted with a data set of over 1,000 papers in the field of DB research to evaluate the performance of this algorithm. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic clustering. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment and achievements. Our method is designed to optimize the reviewer pool for each journal, client and this algorithm is designed with an open license and has the advantage of being free to use and operate. In addition, our algorithm has the advantage of journal-specific optimization by designing to adjust the expertise of the reviewer selection and the weights for exclusion of interests to reflect journal-specific policies. |
| first_indexed | 2024-04-09T14:59:11Z |
| format | Article |
| id | doaj.art-f78eb22dddd0421f85b3f014cd1e0578 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:59:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f78eb22dddd0421f85b3f014cd1e05782023-05-01T23:00:26ZengIEEEIEEE Access2169-35362023-01-0111396093962010.1109/ACCESS.2023.326385710091109An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity AssessmentDong-Hoon Choi0Jin Woo Hyun1Young Rock Kim2https://orcid.org/0000-0001-8575-9099Korea Institute of Science and Technology Information, Seoul, South KoreaDepartment of Mathematics, Graduate School, Hankuk University of Foreign Studies, Seoul, South KoreaGraduate School of Education, Hankuk University of Foreign Studies, Seoul, South KoreaJournal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The affinity can be evaluated by whether any of the reviewers has been a coauthor and/or a coworker in a common institution with any of the authors of the manuscript. The expertise depends on the similarity of the research topic between the reviewer’s published papers and the submitted manuscripts. In this paper we propose an algorithm to recommend appropriate reviewers to editors, based on the assessment of these scholarly activities and achievements. To implement this algorithm, TextRank and GenSim library are used to extract feature sets from abstract and introduction sections of both submitted manuscripts and the reviewer candidates’ papers. Based on the extracted feature sets, affinity and expertise check are implemented. An experiment has been conducted with a data set of over 1,000 papers in the field of DB research to evaluate the performance of this algorithm. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic clustering. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment and achievements. Our method is designed to optimize the reviewer pool for each journal, client and this algorithm is designed with an open license and has the advantage of being free to use and operate. In addition, our algorithm has the advantage of journal-specific optimization by designing to adjust the expertise of the reviewer selection and the weights for exclusion of interests to reflect journal-specific policies.https://ieeexplore.ieee.org/document/10091109/Peer reviewrecommendation algorithmTextRank2-mode networkmax similaritytopic clustering |
| spellingShingle | Dong-Hoon Choi Jin Woo Hyun Young Rock Kim An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment IEEE Access Peer review recommendation algorithm TextRank 2-mode network max similarity topic clustering |
| title | An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment |
| title_full | An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment |
| title_fullStr | An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment |
| title_full_unstemmed | An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment |
| title_short | An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment |
| title_sort | algorithm for peer reviewer recommendation based on scholarly activity assessment |
| topic | Peer review recommendation algorithm TextRank 2-mode network max similarity topic clustering |
| url | https://ieeexplore.ieee.org/document/10091109/ |
| work_keys_str_mv | AT donghoonchoi analgorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment AT jinwoohyun analgorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment AT youngrockkim analgorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment AT donghoonchoi algorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment AT jinwoohyun algorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment AT youngrockkim algorithmforpeerreviewerrecommendationbasedonscholarlyactivityassessment |