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arXiv · 1912.08976

A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

Dong Zhang·Shu Zhao·Zhen Duan·Jie Chen·Yangping Zhang·Jie Tang
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Abstract

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. In this paper, we propose a Multi-Label Classification method using a hierarchical and transparent Representation named Hiepar-MLC. Further, we propose a simple multi-label-based reviewer assignment MLBRA strategy to select the appropriate reviewers. It is interesting that we also explore the paper-reviewer recommendation in the coarse-grained granularity.

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Dong Zhang, Shu Zhao, Zhen Duan, Jie Chen, Yangping Zhang, Jie Tang. 2019-12-19. A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation. https://arxiv.org/abs/1912.08976

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