  We investigate the problem of fair recommendation in the context of two-sided
online platforms, comprising customers on one side and producers on the other.
Traditionally, recommendation services in these platforms have focused on
maximizing customer satisfaction by tailoring the results according to the
personalized preferences of individual customers. However, our investigation
reveals that such customer-centric design may lead to unfair distribution of
exposure among the producers, which may adversely impact their well-being. On
the other hand, a producer-centric design might become unfair to the customers.
Thus, we consider fairness issues that span both customers and producers. Our
approach involves a novel mapping of the fair recommendation problem to a
constrained version of the problem of fairly allocating indivisible goods. Our
proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure
for most of the producers and Envy-Free up to One item (EF1) fairness for every
customer. Extensive evaluations over multiple real-world datasets show the
effectiveness of FairRec in ensuring two-sided fairness while incurring a
marginal loss in the overall recommendation quality.
