A Reviewer Recommender System for Scientific Articles Using a New Similarity Threshold Discovery Technique

Among the tons of articles that are published every year, a considerable number of substandard articles are also published. One of the primary reasons for publishing these substandard articles is due to applying ineffective and/or inefficient reviewer selection processes. To overcome this problem, several reviewer recommender systems are proposed that do not depend on the intelligence of the human selector. However, most of these existing systems do not take the reviewer feedback score or confidence score into consideration during the recommendation process. Therefore, a new reviewer recommender system is proposed in this paper that recommends a set of reviewers to a set of manuscripts with an objective of attaining a high average confidence score taking several constraints into consideration, including a fixed number of reviewers for a manuscript and a fixed number of manuscripts to a reviewer. The proposed system employs a new similarity threshold discovery technique for facilitating the reviewer recommendation process. Again, since there is hardly any dataset exists that satisfies the requirements of the proposed system, a new dataset is prepared by getting the data from various online sources. The proposed system is evaluated by incorporating several existing selection techniques. The experimental results demonstrate that despite employing various selection techniques, the proposed system can assign most of the articles to the prescribed number of reviewers.