CAN: Effective cross features by global attention mechanism and neural network for ad click prediction

Online advertising click-through rate (CTR) prediction is aimed at predicting the probability of a user clicking an ad, and it has undergone considerable development in recent years. One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction. Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors. However, real-world data present a complex and nonlinear structure. Hence, second-order feature interactions are unable to represent cross information adequately. This drawback has been addressed using deep neural networks (DNNs), which enable high-order nonlinear feature interactions. However, DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features. In this study, we propose an effective CTR prediction algorithm called CAN, which explicitly exploits the benefits of attention mechanisms and DNN models. The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions. The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature- and DNN-based predictors.