#### 1. 일시 : 2021년 11월 19일(금), 16:00-18:00 #### 2. 장소 : 광교 테크노밸리 산업수학혁신센터 세미나실 #### 3. 발표자 : 김영록 교수 (한국외국어대학교) #### 4. 주요내용 :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 ted manus. Editors consider whether the reviewers have no affinity with any of the authors of manus and have sufficient expertise in reviewing the manus. 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 manu. The expertise depends on the similarity of the research topic between the reviewer’s published papers and the ted manus. 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 in order to extract feature sets from abstract and introduction sections of both ted manus and the reviewer candidates’ papers. And then based on the extracted feature sets, affinity and expertise check are implemented. To evaluate the performance of this algorithm, an experiment has been conducted with a data set of over 1,000 papers in the field of DB research. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic classification. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment. *코로나19 방역 관련, 방역수칙을 준수하고 최소인원으로 진행하였습니다.