  This paper aims to investigate and achieve seller-side fairness within online
marketplaces, where many sellers and their items are not sufficiently exposed
to customers in an e-commerce platform. This phenomenon raises concerns
regarding the potential loss of revenue associated with less exposed items as
well as less marketplace diversity. We introduce the notion of seller-side
outcome fairness and build an optimization model to balance collected
recommendation rewards and the fairness metric. We then propose a
gradient-based data-driven algorithm based on the duality and bandit theory.
Our numerical experiments on real e-commerce data sets show that our algorithm
can lift seller fairness measures while not hurting metrics like collected
Gross Merchandise Value (GMV) and total purchases.
