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

Author

Listed:
  • Xiaoyu Liu

    (Beijing Electronic Science & Technology Institute)

  • Xuefeng Wang

    (Beijing Institute of Technology)

  • Donghua Zhu

    (Beijing Institute of Technology)

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.

Suggested Citation

  • Xiaoyu Liu & Xuefeng Wang & Donghua Zhu, 2022. "Reviewer recommendation method for scientific research proposals: a case for NSFC," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3343-3366, June.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:6:d:10.1007_s11192-022-04389-4
    DOI: 10.1007/s11192-022-04389-4
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    References listed on IDEAS

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    1. Hendy Abdoul & Christophe Perrey & Philippe Amiel & Florence Tubach & Serge Gottot & Isabelle Durand-Zaleski & Corinne Alberti, 2012. "Peer Review of Grant Applications: Criteria Used and Qualitative Study of Reviewer Practices," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-15, September.
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    7. Wade D. Cook & Boaz Golany & Moshe Kress & Michal Penn & Tal Raviv, 2005. "Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking," Management Science, INFORMS, vol. 51(4), pages 655-661, April.
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    Cited by:

    1. Lin Zhang & Ziyi Tu & Yifei Yu & Yuanyuan Shang & Ying Huang, 2024. "Spotting potential reviewers for interdisciplinary research: insights on active reviewers from Publons," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5533-5556, September.

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    More about this item

    Keywords

    Reviewer recommendation; Knowledge representation; Word embedding; Scientific research proposal selection; Peer review;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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