# PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space

arXiv: https://arxiv.org/abs/1909.11258 · HTML: https://mlsanity.com/paper/1909.11258/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [TWEAC: Transformer with Extendable QA Agent Classifiers](https://mlsanity.com/paper/2104.07081/) (2021)
- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Matching Papers and Reviewers at Large Conferences](https://mlsanity.com/paper/2202.12273/) (2022)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [Chain-of-Factors Paper-Reviewer Matching](https://mlsanity.com/paper/2310.14483/) (2023)
- [Reviewer assignment problem: A scoping review](https://mlsanity.com/paper/2305.07887/) (2023)
- [CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review](https://mlsanity.com/paper/2609.05227/) (2026)
- [An Explanatory Query-Based Framework for Exploring Academic Expertise](https://mlsanity.com/paper/2105.13728/) (2021)
- [Submission-Aware Reviewer Profiling for Reviewer Recommender System](https://mlsanity.com/paper/2211.04194/) (2022)
- [Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition](https://mlsanity.com/paper/2510.12692/) (2025)
- [MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment](https://mlsanity.com/paper/2605.27865/) (2026)

## Abstract (arXiv)

Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches, including bag-of-words models and probabilistic topic models have been inadequate to deal with the vocabulary mismatch and partial topic overlap between a paper submission and the reviewer's expertise. Our approach, the common topic model, jointly models the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to available state-of-the-art implementations of paper-reviewer matching.