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.