Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach

• Published in: Applied sciences Vol. 13; no. 4; p. 2214
• Main Authors: Im, Yunjeong, Song, Gyuwon, Cho, Minsang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Algorithms; Bias; Computational linguistics; conflict of interest; Data mining; Ethical aspects; expert recommendation; highly connected subgraph; Keywords; Language processing; Learned societies; Machine learning; Methods; Natural language interfaces; Natural language processing; Peer review; Productivity; Proposals; R&D; Rankings; Recommender systems; Research & development; Researchers; scholarly big data; Semantics; South Korea
• DOI: 10.3390/app13042214
• ISSN: 2076-3417

Academic societies and funding bodies that conduct peer reviews need to select the best reviewers in each field to ensure publication quality. Conventional approaches for reviewer selection focus on evaluating expertise based on research relevance by subject or discipline. An improved perceiving conflict of interest (CoI) reviewer recommendation process that combines the five expertise indices and graph analysis techniques is proposed in this paper. This approach collects metadata from the academic database and extracts candidates based on research field similarities utilizing text mining; then, the candidate scores are calculated and ranked through a professionalism index-based analysis. The highly connected subgraphs (HCS) algorithm is used to cluster similar researchers based on their association or intimacy in the researcher network. The proposed method is evaluated using root mean square error (RMSE) indicators for matching the field of publication and research fields of the recommended experts using keywords of papers published in Korean journals over the past five years. The results show that the system configures a group of Top-K reviewers with an RMSE 0.76. The proposed method can be applied to the academic society and national research management system to realize fair and efficient screening and management.