A Framework for Optimizing Paper Matching
This addresses the challenge for conference organizers to efficiently assign papers to reviewers, but it is incremental as it builds on existing matching and learning methods.
The paper tackles the problem of matching submitted papers to suitable reviewers at scientific conferences by proposing a framework that uses learned suitability scores and integer programming for assignments, showing performance improvements on two conference datasets.
At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment is a major and important challenge for any conference organizer. In this paper we propose a framework to optimize paper-to-reviewer assignments. Our framework uses suitability scores to measure pairwise affinity between papers and reviewers. We show how learning can be used to infer suitability scores from a small set of provided scores, thereby reducing the burden on reviewers and organizers. We frame the assignment problem as an integer program and propose several variations for the paper-to-reviewer matching domain. We also explore how learning and matching interact. Experiments on two conference data sets examine the performance of several learning methods as well as the effectiveness of the matching formulations.