Forecasting counts of user visits for online display advertising with probabilistic latent class models

Abstract

Display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages have different patterns of user visits. In this paper we propose a probabilistic latent class model to automatically learn the underlying user visit patterns among multiple Web pages. Experiments carried out on real-world data demonstrate the advantage of using latent classes in forecasting online user visits.