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.

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In real world there are a lot of unlabeled data, and relatively few labeled data. Unlabeled data help to learn a statistical model that can fully describe the global property of data, while labeled data help to minimize the gap between the statistical property and human beings' perception, i.e. labeled data can help to learn the semantics. Nonnegative Matrix Factorization is a popular technique in data analysis, since a lot of real world data are nonnegative. However, traditional NMF is an unsupervised learning algorithm, which means that it cannot make use of the label information. To enable NMF to make use of both labeled and unlabeled data samples, we propose a novel semisupervised Nonnegative Matrix Factorization technique for learning the semantics. The proposed algorithm extracts prior information from the labeled data, and then uses it to guide the later processing. Experimental results with different settings prove the efficacy of the proposed algorithm.