Seller-side Outcome Fairness in Online Marketplaces

• URL: http://arxiv.org/abs/2312.03253v1
• Date: Wed, 6 Dec 2023 02:58:49 GMT
• Title: Seller-side Outcome Fairness in Online Marketplaces
• Authors: Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti, Shanu Vashishtha, Jason Cho, Kaushiki Nag, Sushant Kumar, Kannan Achan
• Abstract summary: We introduce the notion of seller-side outcome fairness and build an optimization model to balance collected recommendation rewards and the fairness metric. 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.
• Score: 8.29306513718005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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