Reviewer recommendation method for scientific research proposals: a case for NSFC

Xiaoyu Liu (), Xuefeng Wang and Donghua Zhu
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Xiaoyu Liu: Beijing Electronic Science & Technology Institute
Xuefeng Wang: Beijing Institute of Technology
Donghua Zhu: Beijing Institute of Technology

Scientometrics, 2022, vol. 127, issue 6, No 20, 3343-3366

Abstract: Abstract Peer review is one of the important procedures to determine which research proposals are to be funded and to evaluate the quality of scientific research. How to find suitable reviewers for scientific research proposals is an important task for funding agencies. Traditional methods for reviewer recommendation focus on the relevance of the proposal and knowledge of candidate reviewers by mainly matching the keywords or disciplines. However, the sparsity of keyword space and the broadness of disciplines lead to inaccurate reviewer recommendations. To overcome these limitations, this paper introduces a reviewer recommendation method (RRM) for scientific research proposals. This research applies word embedding to construct vector representation for terms, which provides a semantic and syntactic measurement. Further, we develop representation models for reviewers’ knowledge and proposals, and recommend reviewers by matching two representation models incorporating ranking fusions. The proposed method is implemented and tested by recommending reviewers for scientific research proposals of the National Natural Science Foundation of China. This research invites reviewers to provide feedback, which works as the benchmark for evaluation. We construct three evaluation metrics, Precision, Strict-precision, and Recall. The results show that the proposed reviewer recommendation method highly improves the accuracy. Research results can provide feasible options for the decision-making of the committee, and improve the efficiency of funding agencies.

Keywords: Reviewer recommendation; Knowledge representation; Word embedding; Scientific research proposal selection; Peer review; 68U15 (search for similar items in EconPapers)
JEL-codes: O32 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-022-04389-4

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