Optimizing Reviewer Assignment with Recommender Systems: Models, Related Work, and Evaluation

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.