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

TL;DR

This paper introduces PaRe, a novel common topic model for matching papers to reviewers by capturing shared topics, improving accuracy over previous methods that struggled with vocabulary mismatch and partial topic overlap.

Contribution

The paper presents a new common topic model that jointly models shared topics between papers and reviewer profiles, enhancing matching accuracy.

Findings

Achieves consistent improvements over state-of-the-art methods

Effective in handling vocabulary mismatch and partial topic overlap

Demonstrated on two datasets with positive results

Abstract

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.