  Click through rate (CTR) prediction of image ads is the core task of online
display advertising systems, and logistic regression (LR) has been frequently
applied as the prediction model. However, LR model lacks the ability of
extracting complex and intrinsic nonlinear features from handcrafted
high-dimensional image features, which limits its effectiveness. To solve this
issue, in this paper, we introduce a novel deep neural network (DNN) based
model that directly predicts the CTR of an image ad based on raw image pixels
and other basic features in one step. The DNN model employs convolution layers
to automatically extract representative visual features from images, and
nonlinear CTR features are then learned from visual features and other
contextual features by using fully-connected layers. Empirical evaluations on a
real world dataset with over 50 million records demonstrate the effectiveness
and efficiency of this method.
