Allison Chang

 

In supervised ranking, the goal is to optimize a “rank statistic,” which is a quality measure of a ranked list. We present novel mixed integer optimization (MIO) formulations for supervised ranking problems. Our formulations encompass a large set of rank statistics, including the Area Under the Curve (AUC), the partial or local AUC, and the Discounted Cumulative Gain (DCG).

Other methods for ranking approximate the ranking quality measure by a convex function in order to accommodate extremely large problems, at the expense of exact solutions. As our MIO approach provides exact modeling for ranking problems, our solutions are benchmarks for the other non-exact methods. We report computational results that demonstrate significant advantages for MIO methods over current state-of-the-art.

This is joint work with Dimitris Bertsimas and Cynthia Rudin.