Banner Click Through Rate Classification Using Deep Convolutional Neural Network

Abstract

In banner advertising, Click Through Rate (CTR) is one of the most important indicators to evaluate one advertisement’s quality. Advertisers create massive number of banner candidates in empirical ways, then proceed to actual tests by delivering advertisement to measure each banner’s effectiveness. This process is expensive and therefore our CTR prediction helps reducing online advertising costs. In this work, we propose a method to classify ‘effective’ and ‘ineffective’ advertising banners based on image processing using state-of-the-art CNN. We first focus only on images then conduct experiments including metadata (product, advertiser, etc) to increase the CTR prediction accuracy and demonstrate which metadata is the most influential. Subsequently, each approach is compared to human performance. In the second part of our work, we detect which parts of the image contribute predominantly to increase the CTR by generating heat maps for each classes. This work leads to a deeper understanding of a banner advertising success and helps making decisions on how to improve it.