# Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach
## 1 Introduction  
There has been a global annual increase in the academic research and national research and development (R&D) projects [1; 2; 3]. However, continuous efforts are required to ensure the quality of publications (e.g., research papers, patents, reports, and proposals) and processes that support and manage accurate and effective peer review. Although peer review has been instrumental in academic societies and national research management institutions over the last few years, it remains significantly challenging to provide accurate and effective reviewer recommendations [3; 4; 5; 6; 7; 8]. Manually selecting reviewers from a pool of experts in the field [3; 9] is time-consuming [3; 7; 10; 11] and can suffer from bias. Currently, the delays in the peer review process can be attributed to the time spent by the journal editors in selecting appropriate reviewers, and that spent by the reviewers to accept the requests [9]. Only a small number of researchers are selected because reviewers need to possess a high level of expertise in the target field [6]. Additional reviewers also need to be secured for ensuring fairness in the peer review process and to provide a rapid response to the inseparable process of recruiting reviewers [4; 6; 12]. Several advanced approaches have been recently proposed to improve the quality of recommendations and to support professional reviewers accurately and automatically. Several heuristic algorithms automatically assign reviewers to papers effectively [6; 7]. Further, intelligent decision-support models [4; 8; 13] have improved the overall grouping quality when determining the best reviewer allocation. A content-based reviewer recommendation system supports various information search technologies that evaluate researchproposals or papers [2; 10]. Most of these approaches are based on the reviewer's level of expertise, and they can be considered an optimization problem that ranks the reviewer for a specific objective. In real-world systems, factors such as quality, state-of-the-art, productivity, contribution, persistence, conflict of interest (CoI), and diversity of reviewers should be considered for reviewer selection. Conventional approaches for reviewer selection evaluate expertise by utilizing subject and discipline relevance through simple keyword matching. Most researchers update information on their own in the expert pool, and because research management institutions have no enforcing power to request periodic information updates, the recency and reputation of reviewer recommendations can degrade when only existing information is used. The research topics and fields of interest of a researcher may change over time; however, the history of these research activities is not updated regularly [10; 14], which can lead to an assignment bias. The number of papers published by the researcher in the past is used as a representative indicator of academic contribution. Although important research results are used in addition to the thesis, a more precise calculation method than the simple counting method is required. Attributes such as the quality of the paper, the contribution of the author, and the time of publication should be considered. Another problem is that CoIs often exist in the process of selecting reviewers. Therefore, the potential CoIs among researchers need to be considered to improve the quality and reliability of the peer review processes [3; 4]. For example, a bias in reporting and interpretation can occur if institutions fail to ensure fairness and transparency in the peer review process. These limitations can cause misinterpretations of the author's viewpoint and lead to the rejection of excellent scientific work or a potentially successful project proposal [10]. This paper presents a solution that improves the quality of reviewer recommendation considering the topic's relevance to the reviewer's recent research direction, academic contribution, and CoI.