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An integer linear programming model of reviewer assignment with research interest considerations

Author

Listed:
  • Jian Jin

    (Beijing Normal University)

  • Baozhuang Niu

    (South China University of Technology)

  • Ping Ji

    (The Hong Kong Polytechnic University)

  • Qian Geng

    (Beijing Normal University)

Abstract

In the regular work process of peer review, editors have to read and understand the entire set of submissions to choose appropriate reviewers. However, due to a large number of submissions, to select reviewers manually becomes error-prone and time-consuming. In this research, a framework that considers different indispensable aspects such as topical relevance, topical authority and research interest is presented and, an integer linear programming problem is formulated with practical considerations to recommend reviewers for a group of submissions. Specifically, the topical relevance and the topical authority are utilized to recommend relevant and accredited candidates in submission-related topics, while the research interest is to exam the willingness of candidates to review a submission. To evaluate the effectiveness of the proposed approach, categories of comparative experiments were conducted on two large scholarly datasets. Experimental results demonstrate that, compared with benchmark approaches, the proposed approach is capable to capture the research interest of reviewer candidates without a significant loss in different evaluation metrics. Our work can be helpful for editors to invite matching experts in peer review and highlight the necessity to uncover valuable information from big scholarly data for expert selection.

Suggested Citation

  • Jian Jin & Baozhuang Niu & Ping Ji & Qian Geng, 2020. "An integer linear programming model of reviewer assignment with research interest considerations," Annals of Operations Research, Springer, vol. 291(1), pages 409-433, August.
  • Handle: RePEc:spr:annopr:v:291:y:2020:i:1:d:10.1007_s10479-018-2919-7
    DOI: 10.1007/s10479-018-2919-7
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    References listed on IDEAS

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    1. Lei Li & Yan Wang & Guanfeng Liu & Meng Wang & Xindong Wu, 2015. "Context-Aware Reviewer Assignment for Trust Enhanced Peer Review," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-28, June.
    2. 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.
    3. Robert Setaputra & Xiaohang Yue & Dongqing Yao, 2010. "Impact of Information Systems on Quick Response Programs," International Handbooks on Information Systems, in: T. C. Edwin Cheng & Tsan-Ming Choi (ed.), Innovative Quick Response Programs in Logistics and Supply Chain Management, pages 23-36, Springer.
    4. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
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    Cited by:

    1. Gartner, Daniel & Kolisch, Rainer, 2021. "Mathematical programming for nominating exchange students for international universities: The impact of stakeholders’ objectives and fairness constraints on allocations," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).

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