Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

TL;DR

This paper introduces Deep GSP auctions that use deep learning to optimize multiple conflicting performance metrics in e-commerce advertising, ensuring stable and theoretically sound auction mechanisms.

Contribution

It proposes a novel deep learning-based rank score function within GSP auctions that balances multiple metrics and maintains desirable game-theoretic properties.

Findings

Deep GSP outperforms existing auction mechanisms in offline simulations.

Online A/B tests show improved performance metrics with Deep GSP.

The mechanism maintains stable advertiser utilities across different optimization goals.

Abstract

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitable for e-commerce advertising with various, dynamic, difficult to estimate, and even conflicting performance metrics. In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework. These new rank score functions are implemented via deep neural network models under the constraints of monotone allocation and smooth transition. The requirement of monotone allocation ensures Deep GSP auction nice game theoretical properties, while the requirement of smooth transition guarantees the advertiser utilities would not fluctuate too much when the auction mechanism switches among candidate mechanisms to achieve different optimization objectives. We deployed the proposed mechanisms in a leading e-commerce ad platform and conducted comprehensive experimental evaluations with both offline simulations and online A/B tests. The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms.