Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest
Dublin Core
Title
Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest
Subject
reviewer; conflict of interest (CoI); DBLP dataset; latent dirichlet allocation (LDA); random forest; link prediction
Description
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.
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.
Creator
Adi Setyo Nugroho, Ahmad Saikhu, Ratih Nur Esti Anggraini
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
August 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Adi Setyo Nugroho, Ahmad Saikhu, Ratih Nur Esti Anggraini, “Development of Reviewer Assignment Method with Latent Dirichlet Allocation and Link Prediction to Avoid Conflict of Interest,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10037.