Fairness-aware Guaranteed Display Advertising Allocation under Traffic Cost Constraint

Liang Dai, Zhonglin Zu, Hao Wu, Liang Wang, Bo Zheng

Abstract: Real-time Bidding (RTB) and Guaranteed Display (GD) advertising are two primary ways to sell impressions for publishers in online display advertising. Although GD contract serves less efficiently compared to RTB ads, it helps advertisers reach numerous target audiences at a lower cost and allows publishers to increase overall advertising revenue. However, with billion-scale requests online per day, it’s a challenging problem for publishers to decide whether and which GD ad to display for each impression. In this paper, we propose an optimal allocation model for GD contracts considering optimizing three objectives: maximizing guaranteed delivery and impressions’ quality and minimizing the extra traffic cost of GD contracts to increase overall revenue. The traffic cost of GD contracts is defined as the potential expected revenue if the impression is allocated to RTB ads. Our model dynamically adjusts the weights for each GD contract between impressions’ quality and traffic cost based on real-time performance, which produces fairness-aware allocation results. A parallel training framework based on Parameter-Server (PS) architecture is utilized to efficiently and periodically update the model. Deriving from the allocation model, we also propose a simple and adaptive online bidding strategy for GD contracts, which can be updated quickly by feedback-based algorithms to achieve optimal impression allocation even in complex and dynamic environments. We demonstrate the effectiveness of our proposed method by using both offline evaluation and online A/B testing.