AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings

TL;DR

This paper introduces an algorithm to maximize the AUPCR metric in one-sided preference matchings, addressing limitations of traditional optimality notions and demonstrating its effectiveness on synthetic data.

Contribution

The paper proposes the first algorithm to compute matchings that maximize the AUPCR metric, offering a new pragmatic approach to optimality in preference matchings.

Findings

AUPCR-maximizing matchings outperform traditional notions in experiments.

The proposed algorithm effectively computes AUPCR optimal matchings.

Synthetic data experiments show improved performance over existing methods.

Abstract

We consider the problem of computing a matching in a bipartite graph in the presence of one-sided preferences. There are several well studied notions of optimality which include pareto optimality, rank maximality, fairness and popularity. In this paper, we conduct an in-depth experimental study comparing different notions of optimality based on a variety of metrics like cardinality, number of rank-1 edges, popularity, to name a few. Observing certain shortcomings in the standard notions of optimality, we propose an algorithm which maximizes an alternative metric called the Area under Profile Curve ratio (AUPCR). To the best of our knowledge, the AUPCR metric was used earlier but there is no known algorithm to compute an AUPCR maximizing matching. Finally, we illustrate the superiority of the AUPCR-maximizing matching by comparing its performance against other optimal matchings on synthetic instances modeling real-world data.