The majority of online marketplaces offer promotion programs to sellers to acquire additional customers for their products. These programs typically allow sellers to allocate advertising budgets to promote their products, with higher budgets generally correlating to improve ad performance. Auction mechanisms with budget pacing are commonly employed to implement such ad systems. While auctions deliver satisfactory average effectiveness, ad performance under allocated budgets can be unfair in practice.To address this issue, we propose a novel ad allocation model that departs from traditional auction mechanics. Our approach focuses on solving a global optimization problem that balances traffic allocation while considering platform efficiency and fairness constraints.This study presents the following contributions. First, we introduce a fairness metric based on the Gini index. Second, we formulate the optimization problem incorporating efficiency and fairness objectives. Third, we offer an online algorithm to solve this optimization problem. Finally, we demonstrate that our approach achieves superior fairness compared to baseline auction-based algorithms without sacrificing efficiency. We contend that our proposed method can be effectively applied in real-time ad allocation scenarios and as an offline benchmark for evaluating the fairness-efficiency trade-off of existing auction-based systems.
View on arXiv@article{soboleva2025_2502.01862,
title={ Optimal Traffic Allocation for Multi-Slot Sponsored Search: Balance of Efficiency and Fairness },
author={ Anastasiia Soboleva and Alexander Ledovsky and Yuriy Dorn and Egor Samosvat and Andrey Tikhanov and Fyodor Prazdnikov },
journal={arXiv preprint arXiv:2502.01862},
year={ 2025 }
}