Citation

Lim, Ye Xin and Haw, Su Cheng and Jayaram, Jayapradha (2025) Optimizing Reviewer Assignment with Recommender Systems: Models, Related Work, and Evaluation. International Journal on Robotics, Automation and Sciences, 7 (2). pp. 56-76. ISSN 2682-860X

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Abstract

Peer reviewer assignment to academic articles is important in ensuring the quality and originality of academic publications. Traditional methods of selecting reviewers are generally plagued by inefficiency, reviewer burnout, and inconsistency between the subject of the manuscript and the reviewer area of expertise. In attempting to avoid such drawbacks, recommender systems have been explored as a means of solving the reviewer assignment problem. This article reviews the recommender system techniques in detail by reviewing their application in peer reviewer selection. Additionally, related works shall be examined for how different methods work, their strength and limitations, the dataset used by them, and evaluation metrics used in measuring system performance.

Item Type:	Article
Uncontrolled Keywords:	Recommender system, hybrid-based, peer review
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	11 Nov 2025 04:30
Last Modified:	11 Nov 2025 04:30
URII:	http://shdl.mmu.edu.my/id/eprint/14914

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