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.

翻译：在众多网络应用中，基于深度学习的CTR预测模型（简称深度CTR模型）被广泛采用。传统深度CTR模型以静态方式学习模式，即网络参数在所有实例中保持一致。然而，这种方式难以刻画可能具有不同潜在分布的各个实例，实际上限制了深度CTR模型的表达能力，导致次优结果。本文提出一种高效、通用且实用的模块——自适应参数生成网络（APG），该模块能够根据不同实例动态地为深度CTR模型实时生成参数。大量实验评估结果表明，APG可应用于多种深度CTR模型，并显著提升其性能。同时，与常规深度CTR模型相比，APG可将时间成本降低38.7%，内存使用降低96.6%。我们已将该模块部署于工业级搜索广告系统，分别实现了3%的CTR增益和1%的RPM增益。