Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: APG: Adaptive Parameter Generation Network for Click-Through Rate
  Prediction   In many web applications, deep learning-based CTR prediction models (deep CTR
models for short) are widely adopted. Traditional deep CTR models learn
patterns in a static manner, i.e., the network parameters are the same across
all the instances. However, such a manner can hardly characterize each of the
instances which may have different underlying distribution. It actually limits
the representation power of deep CTR models, leading to sub-optimal results. In
this paper, we propose an efficient, effective, and universal module, Adaptive
Parameter Generation network (APG), where the parameters of deep CTR models are
dynamically generated on-the-fly based on different instances. Extensive
experimental evaluation results show that APG can be applied to a variety of
deep CTR models and significantly improve their performance. We have deployed
APG in the Taobao sponsored search system and achieved 3\% CTR gain and 1\% RPM
gain respectively.

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The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph