Systematic mapping of automated reviewer recommendation solutions

Abstract

The increase in scientific production has generated a recurring problem on a global scale in the recommendation of reviewers for scientific journals and academic events, incentivizing the emergence of a significant diversity of automated solutions. This article presents a systematic review of these reviewer recommendation solutions published in scientific journals and academic events in the period 2018-2023. Methodologically, the final selection focused on the analysis of twenty-five articles. It covered the domain of reviewer recommendation solutions, their methods, factors and the data sets utilized. The results achieved systematize the diverse types of proposed solutions allowing to observe the similarities between the different methods. It is estimated that the present mapping provides an original survey on this problem that provides well-founded comparative information to support future research on reviewer recommendations.

Keywords

• Natural language processing
• Peer Review
• Recovery models
• Selection process