Author–Subject–Topic model for reviewer recommendation. (August 2019)

• Record Type: Journal Article
• Authors: Jin, Jian; Geng, Qian; Mou, Haikun; Chen, Chong
• Is Part Of: Journal of information science. Volume 45:Number 4(2019)
• Page Start: 554
• Page End: 570
• Publication Date: 2019-08
• Subjects: Author–Subject–Topic model -- expert finding -- expert recommendation -- reviewer assignment -- reviewer recommendation; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551518806116
• Languages: English
• ISSNs: 0165-5515

Abstract

Interdisciplinary studies are becoming increasingly popular, and research domains of many experts are becoming diverse. This phenomenon brings difficulty in recommending experts to review interdisciplinary submissions. In this study, an Author–Subject–Topic (AST) model is proposed with two versions. In the model, reviewers' subject information is embedded to analyse topic distributions of submissions and reviewers' publications. The major difference between the AST and Author–Topic models lies in the introduction of a 'Subject' layer, which supervises the generation of hierarchical topics and allows sharing of subjects among authors. To evaluate the performance of the AST model, papers in Information System and Management (a typical interdisciplinary domain) in a famous Chinese academic library are investigated. Comparative experiments are conducted, which show the effectiveness of the AST model in topic distribution analysis and reviewer recommendation for interdisciplinary studies.