Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest

Abstract

The number of published academic papers has been increasing rapidly from year to year. However, this increase in publications must be linear by an emphasis on quality. In order to ensure that academic papers meet the required standard of quality, the process of peer review is necessary. The main goal of reviewer assignment is to find the appropriate reviewer who can conduct a review based on their field of research. However, there are potential obstacles when there is a conflict of interest in the process. This study aims to develop a method for assigning reviewers that overcomes such obstacles. Our approach involves combining the Latent Dirichlet allocation (LDA), classification, and link prediction methods. LDA is used to find topics from the research data of prospective reviewers, to ensure that the assigned reviewers are well-suited to the submitted paper. This data used as training data for classification using Random Forest. Finally, link prediction implemented to make reviewer recommendations. We evaluated and compared our proposed method with previous research that used Cosine similarity for the last step in recommendation, using Mean Average Precision (MAP). Our proposed method achieved a MAP value of 0.87, which was an improvement compared to the previous approach. These results suggest that our approach has the potential to improve the effectiveness of academic peer review.