  Based on the success of recommender systems in e-commerce, there is growing
interest in their use in matching markets (e.g., labor). While this holds
potential for improving market fluidity and fairness, we show in this paper
that naively applying existing recommender systems to matching markets is
sub-optimal. Considering the standard process where candidates apply and then
get evaluated by employers, we present a new recommendation framework to model
this interaction mechanism and propose efficient algorithms for computing
personalized rankings in this setting. We show that the optimal rankings need
to not only account for the potentially divergent preferences of candidates and
employers, but they also need to account for capacity constraints. This makes
conventional ranking systems that merely rank by some local score (e.g.,
one-sided or reciprocal relevance) highly sub-optimal -- not only for an
individual user, but also for societal goals (e.g., low unemployment). To
address this shortcoming, we propose the first method for jointly optimizing
the rankings for all candidates in the market to explicitly maximize social
welfare. In addition to the theoretical derivation, we evaluate the method both
on simulated environments and on data from a real-world
networking-recommendation system that we built and fielded at a large computer
science conference.
