Deep Neural Network-Based Click-Through Rate Prediction using Multimodal Features of Online Banners

Bohui Xia, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa, Hiroyuki Seshime

Abstract: As the online advertisement industry continues to grow, by 2020, it will account for over 40% of global advertisement spending. Thus, predicting the click-through rates (CTRs) of various advertisements is increasingly crucial for companies. Many studies have addressed CTR prediction. How-ever, most tried to solve the problem using only metadata and excluded information such as advertisement images or texts. Using deep learning techniques, we propose a method to predict CTRs for online banners, a popular form of online advertisements, using all these features. We show that multimedia features of advertisements are useful for the task at hand. The proposed learning architecture outperforms a previous method that uses the three features mentioned above. We also present an attention-based model, which enables visualization of contributions of each feature to the prediction. We analyze how each feature affects CTR prediction with visualization and detailed studies.