Click-Through Rate Prediction of Display Advertisement with Deep Multi-Patch Method

Abstract

The online advertising industry continues to grow. Thus, predicting the CTRs increasingly crucial for the industry. Many studies have already addressed the CTR prediction. However, most studies tried to solve the problem using only metadata such as user id, device, etc., and did not include multimedia contents such as images or texts. Using these multimedia features with deep learning techniques, we propose a method to effectively predict CTRs for online banners, a popular form of online advertisements. We show that multimedia features are useful for the task at hand. In our previous work, we proposed a CTR prediction model, which outperformed the state-of-the-art method that uses the three features mentioned above, and also we introduced an attention network for visualizing how much each feature affected the prediction result. In this work, we introduce another text analysis technique and more detailed metadata. Besides, for better analyzing of our model, we introduce another visualization technique to show regions in an image that make its CTR better or worse. Our prediction model gives us useful suggestions for improving design of advertisements to acquire higher CTRs