Optimal Traffic Allocation for Multi-Slot Sponsored Search: Balance of Efficiency and Fairness

TL;DR

This paper introduces a novel ad allocation model that balances platform efficiency and fairness using a Gini index-based metric, offering an online algorithm that improves fairness without compromising effectiveness.

Contribution

It proposes a new fairness-aware optimization framework for ad allocation, departing from traditional auction mechanics, with an online algorithm to implement it.

Findings

Achieves superior fairness compared to baseline auction algorithms.

Maintains efficiency while improving fairness.

Applicable for real-time and offline ad allocation evaluation.

Abstract

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.