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A novel classification method for paper-reviewer recommendation

Author

Listed:
  • Shu Zhao

    (Anhui University)

  • Dong Zhang

    (Anhui University)

  • Zhen Duan

    (Anhui University)

  • Jie Chen

    (Anhui University)

  • Yan-ping Zhang

    (Anhui University)

  • Jie Tang

    (Tsinghua University)

Abstract

Reviewer recommendation problem in the research field usually refers to invite experts to comment on the quality of papers, proposals, etc. How to effectively and accurately recommend reviewers for the submitted papers and proposals is a meaningful and still tough task. At present, many unsupervised recommendation methods have been researched to solve this task. In this paper, a novel classification method named Word Mover’s Distance–Constructive Covering Algorithm (WMD–CCA, for short) is proposed to solve the reviewer recommendation problem as a classification issue. A submission or a reviewer is described by some tags, such as keywords, research interests, and so on. First, the submission or the reviewer is represented as some vectors by a word embedding method. That is to say, each tag describing a submission or a reviewer is represented as a vector. Second, the Word Mover’s Distance (WMD, for short) method is used to measure the minimum distances between submissions and reviewers. Actually, the papers usually have research field information, and utilizing them well might improve the reviewer recommendation accuracy. So finally, the reviewer recommendation task is transformed into a classification problem which is solved by a supervised learning method- Constructive Covering Algorithm (CCA, for short). Comparative experiments are conducted with 4 public datasets and a synthetic dataset from Baidu Scholar, which show that the proposed method WMD–CCA effectively solves the reviewer recommendation task as a classification issue and improves the recommendation accuracy.

Suggested Citation

  • Shu Zhao & Dong Zhang & Zhen Duan & Jie Chen & Yan-ping Zhang & Jie Tang, 2018. "A novel classification method for paper-reviewer recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1293-1313, June.
  • Handle: RePEc:spr:scient:v:115:y:2018:i:3:d:10.1007_s11192-018-2726-6
    DOI: 10.1007/s11192-018-2726-6
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Pradhan, Dinesh K. & Chakraborty, Joyita & Choudhary, Prasenjit & Nandi, Subrata, 2020. "An automated conflict of interest based greedy approach for conference paper assignment system," Journal of Informetrics, Elsevier, vol. 14(2).
    2. 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.
    3. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
    4. Yi Zhang & Fen Zhao & Jianguo Lu, 2019. "P2V: large-scale academic paper embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 399-432, October.

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    More about this item

    Keywords

    Reviewer recommendation; Classification; Word embedding; Word Mover’s Distance; Constructive Covering Algorithm;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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