  In e-commerce advertising, it is crucial to jointly consider various
performance metrics, e.g., user experience, advertiser utility, and platform
revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be
suboptimal due to their fixed allocation rules to optimize a single performance
metric (e.g., revenue or social welfare). Recently, data-driven auctions,
learned directly from auction outcomes to optimize multiple performance
metrics, have attracted increasing research interests. However, the procedure
of auction mechanisms involves various discrete calculation operations, making
it challenging to be compatible with continuous optimization pipelines in
machine learning. In this paper, we design \underline{D}eep \underline{N}eural
\underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing
a differentiable model to relax the discrete sorting operation, a key component
in auctions. We optimize the performance metrics by developing deep models to
efficiently extract contexts from auctions, providing rich features for auction
design. We further integrate the game theoretical conditions within the model
design, to guarantee the stability of the auctions. DNAs have been successfully
deployed in the e-commerce advertising system at Taobao. Experimental
evaluation results on both large-scale data set as well as online A/B test
demonstrated that DNAs significantly outperformed other mechanisms widely
adopted in industry.
