APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
This addresses the problem of sub-optimal performance in click-through rate prediction for web applications by introducing a dynamic parameter generation approach, which is an incremental improvement over existing deep CTR models.
The paper tackles the limitation of static parameters in deep CTR models by proposing the Adaptive Parameter Generation network (APG), which dynamically generates parameters per instance, resulting in a 3% CTR gain and 1% RPM gain in deployment while reducing time cost by 38.7% and memory usage by 96.6%.
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.