Reviewer Assignment Using Weighted Matching and Hungarian Algorithm

Abstract

After closing submission system of the conference, the organizing committees’ duties are huge and among these tasks are assigning at least three reviewers (at least) for each paper. This task involves matching a set of papers against reviewers in order to assign most suitable group of reviewers to specific paper to be reviewed. In this paper, a proposed weighted similarity measure is computed between each pair of (reviewer, author) work. This can be done by keeping a sample of published papers for each reviewer in order to extract the field of expertise of the reviewer and measure the relevance with the author's paper. The proposed weighted similarity measure is based on dividing each paper of authors and reviewers into five sections (i.e. Title, Abstract, Keywords, References, and the rest text in the paper) to compute the matching degree; each paper part is based on its importance thus TF-IDF and cosine similarity techniques are adopted to calculate the degree of correspondence between each pair of coincidence parts. Hereafter, Hungarian optimization algorithm is employed to assign each paper to the most relevance reviewers using computed similarity measure. The experimental results on NTICT 2018 dataset showed that the proposed method achieved its goal.