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

2025-02-03

AI Summary

In multi-slot sponsored search advertising, budget-based allocation mechanisms induce unfairness among advertisers in terms of impression and conversion distribution. Method: This paper proposes an online traffic allocation framework that jointly optimizes platform efficiency and advertiser fairness. It introduces the Gini index—novelly applied to quantify advertising fairness—and formulates a bi-objective constrained optimization model balancing efficiency and fairness. Departing from conventional budget-driven auction designs, the framework solves an online combinatorial optimization problem with explicit fairness constraints. A lightweight, real-time deployable algorithm is developed to ensure no degradation in overall platform revenue while improving fairness. Results: Extensive experiments on multiple real-world datasets demonstrate that the method consistently outperforms mainstream auction baselines in both efficiency and fairness metrics. It offers theoretical rigor, engineering practicality, and offline evaluability.

Technology Category

• Search and Optimization: Algorithm Configuration
• Game Theory and Economic Paradigms: Fair Division
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Optimize traffic allocation in sponsored search
• Balance efficiency and fairness in ad systems
• Introduce Gini index for fairness measurement

Innovation

• Global optimization for traffic allocation
• Gini index as fairness metric
• Online algorithm for real-time application