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.
@article{arxiv.2203.16218,
title = {APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction},
author = {Bencheng Yan and Pengjie Wang and Kai Zhang and Feng Li and Hongbo Deng and Jian Xu and Bo Zheng},
journal= {arXiv preprint arXiv:2203.16218},
year = {2023}
}
Comments
NeurIPS 2022, 16 pages; The first two authors contributed equally to this work