PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
This addresses the time-consuming task for conference organizers by providing a more effective automated matching solution, though it appears incremental as it builds on prior topic modeling approaches.
The paper tackles the problem of automated reviewer-paper matching by proposing a common topic model to address vocabulary mismatch and partial topic overlap, achieving consistent improvements over state-of-the-art methods in experiments on two datasets.
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.