Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction   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.

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The categories of this paper are: supr-con
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int