Analyzing User Behavior in Online Advertising with Facial Expressions

In recent years, more and more online advertisements have been produced to reach large population with reduced advertising cost, and major IT companies largely rely on online advertising for revenue. Hence, for both advertisers and advertisement hosts, effective advertising is of great interest and importance. In this paper, we aim to quantify and predict users' advertisement viewing experiences based on their facial expression responses. We propose a metric termed moment-to- moment zapping probability (MMZP) for predicting users' zapping, i.e., skipping behavior. Experiments are performed on a recently published facial expression dataset for online advertising analysis. By quantifying and analyzing the users' zapping behavior with MMZP, we discover knowledge that may help improve the advertising effectiveness, such as how to present the most suitable advertisements to a user with specific background such as gender and ethnicity.