Seller-side Outcome Fairness in Online Marketplaces
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 6 Dec 2023.
Builds on
16 of 29 references.
References · 16
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4 more
- 2020The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problemscited by 58
- 2018Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systemscited by 37
- 2020Regularized Online Allocation Problems: Fairness and Beyondcited by 22
- 2018A Fairness-aware Hybrid Recommender Systemcited by 18
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