Click-through rate (CTR) prediction is a critical task in online advertising
systems. A large body of research considers each ad independently, but ignores
its relationship to other ads that may impact the CTR. In this paper, we
investigate various types of auxiliary ads for improving the CTR prediction of
the target ad. In particular, we explore auxiliary ads from two viewpoints: one
is from the spatial domain, where we consider the contextual ads shown above
the target ad on the same page; the other is from the temporal domain, where we
consider historically clicked and unclicked ads of the user. The intuitions are
that ads shown together may influence each other, clicked ads reflect a user's
preferences, and unclicked ads may indicate what a user dislikes to certain
extent. In order to effectively utilize these auxiliary data, we propose the
Deep Spatio-Temporal neural Networks (DSTNs) for CTR prediction. Our model is
able to learn the interactions between each type of auxiliary data and the
target ad, to emphasize more important hidden information, and to fuse
heterogeneous data in a unified framework. Offline experiments on one public
dataset and two industrial datasets show that DSTNs outperform several
state-of-the-art methods for CTR prediction. We have deployed the
best-performing DSTN in Shenma Search, which is the second largest search
engine in China. The A/B test results show that the online CTR is also
significantly improved compared to our last serving model.

本文通过研究广告与其他广告之间的关系提高广告点击率（CTR）的预测，分别从空间和时间两个维度考虑辅助广告，提出了一种基于深度时空神经网络（DSTNs）的方法，结合离线和在线实验证明了该方法的有效性和性能优越性。