摘要：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 *** of the hot topics in this area is the construction of feature interactions to facilitate accurate *** machine provides second-order feature interactions by linearly multiplying hidden feature ***,real-world data present a complex and nonlinear ***,second-order feature interactions are unable to represent cross information *** drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature ***,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original *** this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN *** 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 *** experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.