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Recommending Reviewer Groups for Research Projects Based on Topic Coverage |
Liu Xiaoyu1(),Wang Xuefeng2,Zhu Donghua2 |
1Department of Management, Beijing Electronic Science & Technology Institute, Beijing 100070, China 2School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China |
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Abstract [Objective] Aimed at the peer review process of scientific research projects, this paper measures the coverage of reviewers’ knowledge on research project topics and constructs expert groups of maximum topic coverage. [Methods] We proposed three principles for recommending reviewer groups for research projects: the maximum topic coverage principle, the maximum knowledge matching principle, and the appropriate workload principle. Then, we developed a method for identifying the research topics of reviewers and projects using the Overlapping K-means. To achieve maximum topic coverage, we constructed a reviewer group recommendation model based on topic coverage, transforming the recommendation problem into an optimization problem. [Results] In two controlled experiments, the reviewer groups constructed by the proposed method increased the topic coverage by 32.38% and 29.01%, respectively. [Limitations] We need to quantitatively explore how to achieve multi-objective optimization for recommending reviewers for research projects according to the three principles. [Conclusions] This research took the reviewer group recommendation for the National Natural Science Foundation of China project application as a case study. It verified the feasibility and effectiveness of the proposed method through qualitative and quantitative analysis.
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Received: 29 March 2023
Published: 08 January 2024
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Fund:National Natural Science Foundation of China(72104013);National Natural Science Foundation of China(71673024) |
Corresponding Authors:
Liu Xiaoyu,ORCID:0000-0003-2509-8457,E-mail:xiaoyu.liu2019@foxmail.com。
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[1] |
郝红全, 赵英弘, 杨好好, 等. 2022年度国家自然科学基金项目申请、评审与资助工作综述[J]. 中国科学基金, 2023, 37(1): 3-6.
|
[1] |
(Hao Hongquan, Zhao Yinghong, Yang Haohao, et al. Proposal Application, Peer Review and Funding of National Natural Science Foundation of China in 2022: An Overview[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(1): 3-6.)
|
[2] |
张洪亮, 范永刚, 陈青云, 等. 关于完善科学基金评审机制的几点思考[J]. 中国科学基金, 2022, 36(5): 685-692.
|
[2] |
(Zhang Hongliang, Fan Yonggang, Chen Qingyun, et al. Rational Thoughts on Improving the Peer-Review Mechanism During the Deepening Reform of the National Natural Science Foundation[J]. Bulletin of National Natural Science Foundation of China, 2022, 36(5): 685-692.)
|
[3] |
Rahman A I M J, Guns R, Leydesdorff L, et al. Measuring the Match Between Evaluators and Evaluees: Cognitive Distances Between Panel Members and Research Groups at the Journal Level[J]. Scientometrics, 2016, 109(3): 1639-1663.
|
[4] |
李东, 郝艳妮, 彭升辉, 等. 国家自然科学基金委员会信息化建设现状及智能化发展展望[J]. 中国科学基金, 2023, 37(2): 307-312.
|
[4] |
(Li Dong, Hao Yanni, Peng Shenghui, et al. Information Construction of the National Natural Science Foundation of China: Today and Intelligent Development Prospects[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(2): 307-312.)
|
[5] |
杨好好, 郝红全, 安浩, 等. 新时期国家自然科学基金分类申请与评审改革成效[J]. 中国科学基金, 2022, 36(5): 675-684.
|
[5] |
(Yang Haohao, Hao Hongquan, An Hao, et al. The Importance of the Reform on Classification Application and Evaluation of National Natural Science Foundation of China in the New Era[J]. Bulletin of National Natural Science Foundation of China, 2022, 36(5): 675-684.)
|
[6] |
李江涛, 刘雷, 施玲, 等. 2022年度管理科学部基金项目评审工作综述[J]. 中国科学基金, 2023, 37(1): 48-53.
|
[6] |
(Li Jiangtao, Liu Lei, Shi Ling, et al. Proposal Application, Peer Review and Funding of the Department of Management Sciences in 2022: An Overview[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(1): 48-53.)
|
[7] |
Wang Q, Ma J, Liao X W, et al. A Context-Aware Researcher Recommendation System for University-Industry Collaboration on R&D Projects[J]. Decision Support Systems, 2017, 103: 46-57.
|
[8] |
Liu O, Wang J, Ma J, et al. An Intelligent Decision Support Approach for Reviewer Assignment in R&D Project Selection[J]. Computers in Industry, 2016, 76: 1-10.
|
[9] |
van Arensbergen P, van der Weijden I, van den Besselaar P. The Selection of Talent as a Group Process. A Literature Review on the Social Dynamics of Decision Making in Grant Panels[J]. Research Evaluation, 2014, 23(4): 298-311.
|
[10] |
Hoang D T, Nguyen N T, Hwang D. Recommendation of Expert Group to Question and Answer Sites Based on User Behaviors and Diversity[J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(6): 7117-7129.
|
[11] |
于璇, 高瑞平. 发挥国家自然科学基金联合基金“四个平台”作用健全基础研究多元投入机制[J]. 中国科学基金, 2023, 37(2): 296-300.
|
[11] |
(Yu Xuan, Gao Ruiping. Give Play to the Role of the “Four Platforms” of the Joint Funds of NSFC, Improve the Diversified Input Mechanism for Basic Research[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(2): 296-300.)
|
[12] |
周小梅, 聂建青, 唐福杰, 等. 国家自然科学基金联合基金管理机制:问题与建议[J]. 中国科学基金, 2023, 37(1): 131-135.
|
[12] |
(Zhou Xiaomei, Nie Jianqing, Tang Fujie, et al. On the Joint Fund Management Mechanism of National Natural Science Foundation of China: Problems and Suggestions[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(1): 131-135.)
|
[13] |
李正风, 武晨箫, 黄璐, 等. 国家自然科学基金如何更好地引导基础研究多元投入?[J]. 中国科学院院刊, 2021, 36(12): 1448-1455.
|
[13] |
(Li Zhengfeng, Wu Chenxiao, Huang Lu, et al. How Could National Natural Science Foundation of China Better Guide Diversified Investment in Basic Research?[J]. Bulletin of Chinese Academy of Sciences, 2021, 36(12): 1448-1455.)
|
[14] |
Herrera F, Martı́nez L, Sánchez P J. Managing Non-Homogeneous Information in Group Decision Making[J]. European Journal of Operational Research, 2005, 166(1): 115-132.
|
[15] |
Ceylan D, Saatçioǧlu Ö, Sepil C. An Algorithm for the Committee Construction Problem[J]. European Journal of Operational Research, 1994, 77(1): 60-69.
|
[16] |
Silva T, Guo Z L, Ma J, et al. A Social Network-Empowered Research Analytics Framework for Project Selection[J]. Decision Support Systems, 2013, 55(4): 957-968.
|
[17] |
Darling E S. Use of Double-Blind Peer Review to Increase Author Diversity[J]. Conservation Biology, 2015, 29(1): 297-299.
doi: 10.1111/cobi.12333
pmid: 25039807
|
[18] |
赵宋焘, 申茜, 戴亚飞, 等. 2022年度交叉科学部基金项目评审工作综述[J]. 中国科学基金, 2023, 37(1): 54-56.
|
[18] |
(Zhao Songtao, Shen Qian, Dai Yafei, et al. Proposal Applications, Peer Review and Funding of the Department of Interdisciplinary Sciences in 2022: An Overview[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(1): 54-56.)
|
[19] |
李文聪, 徐进, 申洁, 等. 英国国家科研与创新署学科交叉研究资助机制及启示[J]. 物理化学学报, 2020, 36(11): 173-178.
|
[19] |
(Li Wencong, Xu Jin, Shen Jie, et al. Interdisciplinary Research Funding Mechanisms of the UK Research and Innovation(UKRI) and Their Implications[J]. Acta Physico-Chimica Sinica, 2020, 36(11): 173-178.)
|
[20] |
贺颖. 基于科学计量视角的同行评议专家遴选问题研究[D]. 天津: 天津大学, 2008.
|
[20] |
(He Ying. Study on the Problems About Expert Selection of Peer Review Based on the Viewpoint of Scientometrics[D]. Tianjin: Tianjin University, 2008.)
|
[21] |
李江, 李东, 冯培桦, 等. 基于专长吻合度、学术影响力与社会关联值的专家推荐模型研究[J]. 情报学报, 2017, 36(4): 338-345.
|
[21] |
(Li Jiang, Li Dong, Feng Peihua, et al. An Expert Recommendation Model Based on the Speciality, Scientific Impact of Experts, and Social Relationship Between Experts and Applicants[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(4): 338-345.)
|
[22] |
吴仁克. 科技项目评审专家智能检索与推荐系统的研究及实现[D]. 杭州: 杭州电子科技大学, 2014.
|
[22] |
(Wu Renke. Study on Intelligent Retrieval and Recommendation System for Science and Technology Project Experts[D]. Hangzhou: Hangzhou Dianzi University, 2014.)
|
[23] |
Li X L, Watanabe T. Paper-to-Reviewer Assignment, Based on Expertise Degree of Reviewers and Relevance Degree Between Reviewers and Papers[J]. International Journal of Knowledge and Web Intelligence, 2014, 5(1): 1-20.
|
[24] |
Protasiewicz J, Pedrycz W, Kozłowski M, et al. A Recommender System of Reviewers and Experts in Reviewing Problems[J]. Knowledge-Based Systems, 2016, 106: 164-178.
|
[25] |
Liu X Y, Wang X F, Zhu D H. Reviewer Recommendation Method for Scientific Research Proposals: A Case for NSFC[J]. Scientometrics, 2022, 127(6): 3343-3366.
|
[26] |
Kunaver M, Porl T. Diversity in Recommender Systems—A Survey[J]. Knowledge-Based Systems, 2017, 123: 154-162.
|
[27] |
Nguyen N T. Advanced Methods for Inconsistent Knowledge Management[M]. London: Springer London, 2008.
|
[28] |
Chen Y, Fan Z P, Ma J, et al. A Hybrid Grouping Genetic Algorithm for Reviewer Group Construction Problem[J]. Expert Systems with Applications, 2011, 38(3): 2401-2411.
|
[29] |
Cleuziou G. An Extended Version of the K-Means Method for Overlapping Clustering[C]// Proceedings of the 19th International Conference on Pattern Recognition. IEEE, 2008.
|
[30] |
De Mulder W. Optimal Clustering in the Context of Overlapping Cluster Analysis[J]. Information Sciences, 2013, 223: 56-74.
|
[31] |
Ben n’cir C E, Cleuziou G, Essoussi N. Overview of Overlapping Partitional Clustering Methods[M]// CelebiM. PartitionalClustering Algorithms. Cham: Springer, 2015: 245-275.
|
[32] |
Banerjee A, Krumpelman C, Ghosh J, et al. Model-Based Overlapping Clustering[C]// Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 2005: 532-537.
|
[33] |
刘晓豫, 朱东华, 汪雪锋, 等. 多专长专家识别方法研究——以大数据领域为例[J]. 图书情报工作, 2018, 62(3): 55-63.
doi: 10.13266/j.issn.0252-3116.2018.03.007
|
[33] |
(Liu Xiaoyu, Zhu Donghua, Wang Xuefeng, et al. Multi-Expertise Researcher Identification: A Case Study of the Big Data[J]. Library and Information Service, 2018, 62(3): 55-63.)
doi: 10.13266/j.issn.0252-3116.2018.03.007
|
[34] |
吴广建, 章剑林, 袁丁. 基于K-means的手肘法自动获取K值方法研究[J]. 软件, 2019, 40(5): 167-170.
|
[34] |
(Wu Guangjian, Zhang Jianlin, Yuan Ding. Automatically Obtaining K Value Based on K-Means Elbow Method[J]. Computer Engineering & Software, 2019, 40(5): 167-170.)
|
[35] |
Calinski T, Harabasz J. A Dendrite Method for Cluster Analysis[J]. Communications in Statistics, 1974, 3(1): 1-27.
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