APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction

Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, Bo Zheng

Abstract:

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.