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arXiv · 2312.03253

Seller-side Outcome Fairness in Online Marketplaces

Zikun Ye·Reza Yousefi Maragheh·Lalitesh Morishetti·Shanu Vashishtha·Jason Cho·Kaushiki Nag·Sushant Kumar·Kannan Achan
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Abstract

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.

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Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti, Shanu Vashishtha, Jason Cho, Kaushiki Nag, Sushant Kumar, Kannan Achan. 2023-12-06. Seller-side Outcome Fairness in Online Marketplaces. https://arxiv.org/abs/2312.03253

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