  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.
