Proposal reviewer recommendation system based on big data for a national research management institute

Abstract

National research management organizations need to ensure that research proposals are reviewed fairly and efficiently, which requires the selection of suitable reviewers. In particular, reviewing research proposals in a particular area necessitates the selection of a group with the most reasonable standard for recommending an expert in that area. In this study, we develop an automatic matching system that matches a research proposal with a reviewer who can evaluate it most effectively, using keywords with fuzzy weights based on databases in the corresponding field of research. All functions that we developed were based on the MapReduce framework created by Hadoop, which was verified to enhance matching performance and ensure expandability. This enabled us to select suitable researchers from existing research projects, papers and research reviewer databases. Our system can influence the operation of the national research management system and contribute to academic development.

Keywords

Big data fuzzy weight keyword search research proposal reviewer

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