  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 distributions. 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, named as
Adaptive Parameter Generation network (APG), which can dynamically generate
parameters for deep CTR models 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.
Meanwhile, APG can reduce the time cost by 38.7\% and memory usage by 96.6\%
compared to a regular deep CTR model. We have deployed APG in the industrial
sponsored search system and achieved 3\% CTR gain and 1\% RPM gain
respectively.
