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Workshop: Algorithmic Fairness through the Lens of Time
Seller-side Outcome Fairness in Online Marketplaces
Zikun Ye · Reza Yousefi Maragheh · Lalitesh Morishetti · Shanu Vashishtha · Jason Cho · Kaushiki Nag · Sushant Kumar · Kannan Achan
Fri 15 Dec 7 a.m. PST — 3:30 p.m. PST
This paper aims to investigate and address the issue of 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 measure. 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 CTR.