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Finding the right reviewers to assess the quality of conference submissionsis a time consuming process for conference organizers. Given the importance ofthis step, various automated reviewer-paper matching solutions have beenproposed to alleviate the burden. Prior approaches, including bag-of-wordsmodels and probabilistic topic models have been inadequate to deal with thevocabulary mismatch and partial topic overlap between a paper submission andthe reviewer's expertise. Our approach, the common topic model, jointly modelsthe topics common to the submission and the reviewer's profile while relying onabstract topic vectors. Experiments and insightful evaluations on two datasetsdemonstrate that the proposed method achieves consistent improvements comparedto available state-of-the-art implementations of paper-reviewer matching.

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