Seller-side Outcome Fairness in Online Marketplaces
Zikun Ye Reza Yousefi Maragheh Lalitesh Morishetti Shanu Vashishtha Jason Cho Kaushiki Nag Sushant Kumar Kannan Achan
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.