# Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach
## 2 Related Work
### Review Recommendation System
#### 2.1.2 Review Recommendation  
The emergence of big data generated by scientific writing has inspired researchers to use such data to facilitate the tasks involved in the research process. Reviewer recommendation procedures involve recognizing expertise by tracking research experience or interest in the target field [10; 16]. Further, many researchers leave the results of the research field such as papers, patents, and research reports. Given this information, studies where reviewer recommendations are provided by judging the expertise of researchers are actively being studied, and they fall into three categories:  
1. Profile-centric: In this category, the reviewers' profiles are generated and matched with the keywords of the papers to be reviewed. The methods used in this approach range from information retrieval, subject modeling, and natural language processing to self-evaluation and bidding (preferred base). Thus far, several studies have been conducted to allocate reviewers to manuscripts using methods such as web mining, latent semantic indexing [2; 21], topic modeling, and fuzzy modeling. For example, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [10] proposed a content-based recommendation system that selects reviewers to evaluate research proposals or papers. The information search process includes publication classification, author clarification, keyword extraction, and total text indexing. The recommendation method combines cosine similarity among keywords, and total text indexing is suggested. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [20] proposed the topic-specific contextual feature model that analyzes users' social activities and recommends reviewers. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [21] proposed an ontology-based model using links open data to recommend reviewers. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [6] proposed a greedy algorithm that ranks each candidate reviewer through various scores, such as subject consistency, up-to-date, and quality, using an ordered weighted averaging (OWA) operator. The research interests of the reviewers are taken directly from the mentioned website and sorted (automatically or by a reviewer) based on the topic of interest. Finally, the thesis-reviewer similarity is calculated as an OWA function of reviewer expertisebased on the thesis's topic coverage needs, date, quality, and reviewer availability. [PERSON] et al. (2018) presented thematic modeling methods such as extracting topics from the text content of papers and reviewers using probabilistic latent semantic analysis; they clustered reviewers' publications and used cosine distances to identify potential research areas most similar to query papers and reviewers. The relevance score was then calculated using the Kullback-Leibler branch measurement. However, matching people and objects efficiently using cosine measurements between keywords can be challenging because other terms may have similar meanings, which can make fitting difficult. Therefore, [PERSON] et al. (2018) proposed fuzzy logic to represent the expertise level of reviewers in different areas. The expertise level is calculated based on the reviewer's workload balance, CoI prevention, and individual preferences through multiple keyword mapping of the proposal. [PERSON] et al. (2018) proposed a system that measures similarity using keywords with weights based on fuzzy logic, and they automatically matched research proponents and reviewers. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2018) used the word2 vec-based word mover distance algorithm for calculating the text distance between the reviewers' research interests and manuscripts, and they used the label information to classify reviewers. These methods determine the subject relationship between the reviewer and the manuscript through textual information. 2. Group-centric: This category clusters the manuscripts into groups based on topic similarity and then assigns reviewers.