Seller-side Outcome Fairness in Online Marketplaces

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 ite...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Ye, Zikun, Reza Yousefi Maragheh, Morishetti, Lalitesh, Vashishtha, Shanu, Cho, Jason, Nag, Kaushiki, Kumar, Sushant, Kannan Achan
Format: Article
Language:English
Subjects:
Algorithms
Electronic commerce
Optimization models
Online Access:Get full text
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Summary: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.
ISSN:2331-8422