  For the scalability of industrial online advertising systems, a two-stage
auction architecture is widely used to enable efficient ad allocation on a
large set of corpus within a limited response time. The current deployed
two-stage ad auction usually retrieves an ad subset by a coarse ad quality
metric in a pre-auction stage, and then determines the auction outcome by a
refined metric in the subsequent stage. However, this simple and greedy
solution suffers from serious performance degradation, as it regards the
decision in each stage separately, leading to an improper ad selection metric
for the pre-auction stage. In this work, we explicitly investigate the relation
between the coarse and refined ad quality metrics, and design a two-stage ad
auction by taking the decision interaction between the two stages into account.
We decouple the design of the two-stage auction by solving a stochastic subset
selection problem in the pre-auction stage and conducting a general second
price (GSP) auction in the second stage. We demonstrate that this decouple
still preserves the incentive compatibility of the auction mechanism. As the
proposed formulation of the pre-auction stage is an NP-hard problem, we propose
a scalable approximation solution by defining a new subset selection metric,
namely Pre-Auction Score (PAS). Experiment results on both public and
industrial dataset demonstrate the significant improvement on social welfare
and revenue of the proposed two-stage ad auction, than the intuitive greedy
two-stage auction and other baselines.
