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.

检测和实现在线市场中卖方公平性，通过引入卖方结果公平性概念和构建以平衡推荐奖励和公平度度量为目标的优化模型，然后提出基于数据驱动的梯度下降算法，通过对真实电子商务数据集进行数值实验，发现该算法能提升卖方公平性而不损害总采购额和总购买次数等指标。