A novel classification method for paper-reviewer recommendation

  • Shu Zhao
  • Dong Zhang
  • Zhen Duan
  • Jie Chen
  • Yan-ping Zhang
  • Jie Tang
Article
  • 109 Downloads

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.

Keywords

Reviewer recommendation Classification Word embedding Word Mover’s Distance Constructive Covering Algorithm 

Mathematics Subject Classification

68T99 

JEL Classification

C63 C89 

Notes

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grants #61402006, #61602003 and #61673020), National High Technology Research and Development Program (863 Plan)(Grant #2015AA124102), Innovation Zone Project Program for Science and Technology of China’s National Defense (Grant No. 2017-0001-863015-0009), the National Key Research and Development Program of China (2017YFB1401903), the Provincial Natural Science Foundation of Anhui Province (Grants #1508085MF113 and #1708085QF156), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (Forty-ninth batch) and the Recruitment Project of Anhui University for Academic and Technology Leader.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  • Shu Zhao
    • 1
  • Dong Zhang
    • 1
  • Zhen Duan
    • 1
  • Jie Chen
    • 1
  • Yan-ping Zhang
    • 1
  • Jie Tang
    • 2
  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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