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.
Cite
@article{arxiv.1912.08976,
title = {A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation},
author = {Dong Zhang and Shu Zhao and Zhen Duan and Jie Chen and Yangping Zhang and Jie Tang},
journal= {arXiv preprint arXiv:1912.08976},
year = {2019}
}