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

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