  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.
