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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (6): 132-143    DOI: 10.11925/infotech.2096-3467.2023.0270
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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.

Key wordsReview of Scientific Research Projects      Reviewer Group      Reviewer Recommendation      Topic Coverage     
Received: 29 March 2023      Published: 08 January 2024
ZTFLH:  G316  
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。   

Cite this article:

Liu Xiaoyu, Wang Xuefeng, Zhu Donghua. Recommending Reviewer Groups for Research Projects Based on Topic Coverage. Data Analysis and Knowledge Discovery, 2024, 8(6): 132-143.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2023.0270     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I6/132

Research Framework
The Number of Chinese Document Cluster(the Elbow Method)
The Number of Chinese Document Cluster(the Calinski-Harabasz Method)
The Number of English Document Cluster(the Elbow Method)
The Number of English Document Cluster(the Calinski-Harabasz Method)
序号 项目 专家 机构
1 产学研合作成效影响机理及提升策略研究 谢专家 上海交通大学
杜专家 浙江工业大学
刘专家 中国农业科学院农业资源与农业区划研究所
朱专家 华南理工大学
周专家 教育部科技发展中心
2 考虑利益相关者行为的协同创新绩效研究 眭专家 中国科学院科技战略咨询研究院
孙专家 哈尔滨商业大学
朱专家 华南理工大学
于专家 哈尔滨工业大学
马专家 同济大学
3 海外引进人才的科研合作行为及其影响因素研究:以长江学者为例 杜专家 浙江工业大学
陈专家 中国科学院大学
邢专家 科技部科技评估中心
马专家 华南理工大学
郑专家 中国人民解放军军事医学科学院
4 众包协同创新网络的资源集成模式及其对产品创新绩效的作用机理 洪专家 大连理工大学
汝专家 清华大学
朱专家 华南理工大学
孙专家 大连理工大学
邢专家 科技部科技评估中心
5 适宜性创新模式选择与全要素生产率提升:基于创新价值链与空间外溢视角 余专家 中国科学院科技战略咨询研究院
邢专家 科技部科技评估中心
吴专家 浙江大学
白专家 南京师范大学
汝专家 清华大学
6 创新能力结构视角下跨界搜寻对组织双元能力的影响机理研究 方专家 北京航空航天大学
黄专家 北京理工大学
杨专家 清华大学
汝专家 清华大学
谈专家 上海交通大学
7 行业政策能力与企业创新能力的协同演进机理研究——对中国机床工业的经验研究 杨专家 清华大学
刘专家 中国农业科学院农业资源与农业区划研究所
杨专家 杭州电子科技大学
唐专家 复旦大学
黄专家 北京理工大学
8 专利密度视角下专利制度影响我国产业经济发展的传导机制研究 汝专家 清华大学
官专家 复旦大学
余专家 中国科学院科技战略咨询研究院
眭专家 中国科学院科技战略咨询研究院
吴专家 浙江大学
9 基于专利布局战略与社会网络分析观点探讨影响专利价值的因素研究 乔专家 厦门大学
陈专家 北京航空航天大学
杜专家 浙江工业大学
冷专家 中国科学院文献情报中心
林专家 南京理工大学
10 面向科技监测的实体识别与关系抽取研究 栾专家 大连理工大学
王专家 东北林业大学
侯专家 大连大学
张专家 武汉大学
王专家 大连理工大学
11 产学研合作成效影响机理及提升策略研究 唐专家 复旦大学
王专家 大连理工大学
潘专家 中国科学技术信息研究所
侯专家 大连大学
何专家 中国医科大学
12 政府科研项目资助的间接成本补偿研究:一种基于机构特征的分类补偿模型 刘专家 大连理工大学
周专家 杭州电子科技大学
杜专家 浙江工业大学
张专家 湖南大学
杨专家 杭州电子科技大学
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