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

arXiv: https://arxiv.org/abs/1912.08976 · HTML: https://mlsanity.com/paper/1912.08976/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Chain-of-Factors Paper-Reviewer Matching](https://mlsanity.com/paper/2310.14483/) (2023)
- [RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training](https://mlsanity.com/paper/2110.07336/) (2021)
- [RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation](https://mlsanity.com/paper/2407.20684/) (2024)
- [SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification](https://mlsanity.com/paper/2302.09683/) (2023)
- [HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification](https://mlsanity.com/paper/2402.01696/) (2024)
- [exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem](https://mlsanity.com/paper/2502.07683/) (2025)
- [Reviewer assignment problem: A scoping review](https://mlsanity.com/paper/2305.07887/) (2023)
- [Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens](https://mlsanity.com/paper/2309.04333/) (2023)
- [OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation](https://mlsanity.com/paper/2602.08896/) (2026)
- [Minimal Learning Machine for Multi-Label Learning](https://mlsanity.com/paper/2305.05518/) (2023)
- [MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment](https://mlsanity.com/paper/2605.27865/) (2026)

## Abstract (arXiv)

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.