DEEP NEURAL NETWORK-BASED CLICK-THROUGH RATE PREDICTION USING MULTIMODAL FEATURES OF ONLINE BANNERS

As the online advertisement industry continues to grow, by 2020, it will account for over 40% of global advertisement spending 1. Thus, predicting the click-through rates (CTRs) of various advertisements is increasingly crucial for companies. Many studies have addressed CTR prediction. However, 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.