A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
This addresses the challenge of recommending reviewers for academic papers, which is important for conference chairs and journal editors, but appears incremental as it builds on existing multi-label classification approaches.
The authors tackled the paper-reviewer recommendation problem by proposing Hiepar-MLC, a multi-label classification method with a hierarchical and transparent representation, and MLBRA, a strategy for reviewer assignment, achieving effective and accurate recommendations, though no concrete numbers are provided.
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.