An automated conflict of interest based greedy approach for conference paper assignment system

Reviewer Assignment Problem (RAP) is a crucial problem for the conference due to time constraints and inadequate availability of expert reviewers. A fair evaluation of paper is key to an author's success, paper quality, conference reputation, and productive usage of funds. Recent studies reflect on the issue of reviewer bias in bids favoring authors belonging to the top institution and higher authority. Existing Conference Management Systems (CMS) are solely dependent upon self-declared Conflict of Interest (CoI) made by the authors, and reviewers. In literature, existing studies considers topic similarity, potential CoI, and reviewer's workload as trivial factors for ensuring review quality. Other factors include the diversity and authority of a reviewer. Past studies propose several theoretical optimization models. In this paper, we first individually model the factors using the best possible strategy in a constrained-based optimization framework. We tried to propose a completely novel framework that can be practically implemented to improve upon the performance of existing CMS. We map the RAP to an equilibrium multi-job assignment problem. Moreover, we propose a meta-heuristic greedy solution to solve it using weighted matrix factorization. We re-define an assignment quality metric required to validate such assignments. A real conference assignment data set collected from EasyChair is used for a comparative study. The TPMS is used as a baseline because it also uses similar factors, and due to its integration with widely used Microsoft CMS. The results show that the mean assignment quality of the proposed method is superior to other benchmark RAP systems.