A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2019
- DOI:
- 10.48550/arXiv.1912.08976
- arXiv:
- arXiv:1912.08976
- Bibcode:
- 2019arXiv191208976Z
- Keywords:
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- Computer Science - Information Retrieval
- E-Print:
- 21 pages