Click-Through Rate Prediction of Online Banners Featuring Multimodal Analysis

Abstract

As the online advertisement industry continues to grow, it is predicted that online advertisement will account for about 45% of global advertisement spending by 2020.a Thus, predicting the click-through rates (CTRs) of advertisements is increasingly crucial for the advertisement industry. Many studies have already addressed the CTR prediction. However, most studies tried to solve the problem using only metadata such as user id, URL of the landing page, business category, device type, 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 of advertisements are useful for the task at hand. In our previous work [ 1 ], 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. As a result, we have achieved much better performance as compared to our previous work. 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.