  Contemporary recommender systems act as intermediaries on multi-sided
platforms serving high utility recommendations from sellers to buyers. Such
systems attempt to balance the objectives of multiple stakeholders including
sellers, buyers, and the platform itself. The difficulty in providing
recommendations that maximize the utility for a buyer, while simultaneously
representing all the sellers on the platform has lead to many interesting
research problems.Traditionally, they have been formulated as integer linear
programs which compute recommendations for all the buyers together in an
\emph{offline} fashion, by incorporating coverage constraints so that the
individual sellers are proportionally represented across all the recommended
items. Such approaches can lead to unforeseen biases wherein certain buyers
consistently receive low utility recommendations in order to meet the global
seller coverage constraints. To remedy this situation, we propose a general
formulation that incorporates seller coverage objectives alongside individual
buyer objectives in a real-time personalized recommender system. In addition,
we leverage highly scalable submodular optimization algorithms to provide
recommendations to each buyer with provable theoretical quality bounds.
Furthermore, we empirically evaluate the efficacy of our approach using data
from an online real-estate marketplace.
