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Peer review is one of the most crucial and important tasks that are associated with academic conferences, journals and grant proposals; and assignment of an appropriate reviewer plays vital role for accurate and fair review process. This paper presents a learning based proactive system that assigns reviewer(s) whose expertise matches with the domain(s) of the paper satisfying constraints. The assignment of reviewer to paper needs to satisfy various constraints such as maximum number of papers per reviewer, minimum number of reviewers per paper and conflict of interest. he core challenge in reviewer paper assignment is to make the computer understand the subject domain of experts and papers. In proposed system, features are extracted from title, abstract and introduction section of publications of reviewer and submitted papers. These features help the model learn the domain features of experts and submitted papers more accurately. Once the training set is built utilizing the inherent correlation between abstract and title, the model is trained and the similarity between reviewers and papers is predicted. The experimental results on test data set of AAAI 2014 and NIPS 2019 demonstrate the effectiveness of the proposed system.

Fair and Accurate Review in Publication Process: A learning-based Proactive Approach for Assigning Reviewers to Manuscripts