Viewability prediction for online display ads

As a massive industry, display advertising delivers advertisers' marketing messages to attract customers through graphic banners on webpages. Advertisers are charged by ad serving, where their ads are shown in web pages. However, recent studies show that about half of the ads were actually never seen by users because they do not scroll deep enough to bring the ads in-view. Thus, the ad pricing standards are shifting to a new model: ads are paid if they are in view, not just being served. To the best of our knowledge, this paper is the first to address the important problem of ad viewability prediction which can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. We analyze a real-life dataset from a large publisher, identify a number of features that impact the scroll depth for a given user and a page, and propose a probabilistic latent class model that predicts the viewability of any given scroll depth for a user-page pair. The experiments demonstrate that our model outperforms comparison systems based on singular value decomposition and logistic regression, in terms of prediction quality and training time.