Forecasting user visits for online display advertising

Online 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 and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.

[1]  Andrei Z. Broder,et al.  A search-based method for forecasting ad impression in contextual advertising , 2009, WWW '09.

[2]  Andrei Z. Broder,et al.  A semantic approach to contextual advertising , 2007, SIGIR.

[3]  Ashish Goel,et al.  Truthful auctions for pricing search keywords , 2006, EC '06.

[4]  Timothy W. Finin,et al.  Yahoo! as an ontology: using Yahoo! categories to describe documents , 1999, CIKM '99.

[5]  Wei Li,et al.  A stochastic learning-to-rank algorithm and its application to contextual advertising , 2011, WWW.

[6]  Luo Si,et al.  Discriminative probabilistic models for expert search in heterogeneous information sources , 2011, Information Retrieval.

[7]  Vassilis Plachouras,et al.  A noisy-channel approach to contextual advertising , 2007, ADKDD '07.

[8]  A. Zellner,et al.  A Note on Aggregation, Disaggregation and Forecasting Performance , 2000 .

[9]  Rong Yan,et al.  Probabilistic latent query analysis for combining multiple retrieval sources , 2006, SIGIR.

[10]  David S. Stoffer,et al.  Time series analysis and its applications , 2000 .

[11]  Erik Vee,et al.  Pricing guaranteed contracts in online display advertising , 2010, CIKM.

[12]  Luo Si,et al.  Forecasting counts of user visits for online display advertising with probabilistic latent class models , 2011, SIGIR '11.

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  FangYi,et al.  Discriminative probabilistic models for expert search in heterogeneous information sources , 2011 .

[15]  Luo Si,et al.  Identifying similar people in professional social networks with discriminative probabilistic models , 2011, SIGIR.

[16]  Berthier A. Ribeiro-Neto,et al.  Impedance coupling in content-targeted advertising , 2005, SIGIR '05.

[17]  Evgeniy Gabrilovich,et al.  Translating relevance scores to probabilities for contextual advertising , 2009, CIKM.

[18]  Datong Chen,et al.  Forecasting high-dimensional data , 2010, SIGMOD Conference.

[19]  Deepayan Chakrabarti,et al.  Contextual advertising by combining relevance with click feedback , 2008, WWW.

[20]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[21]  Rajiv Khanna,et al.  Estimating rates of rare events with multiple hierarchies through scalable log-linear models , 2010, KDD '10.

[22]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[23]  Robert H. Shumway,et al.  Time Series Analysis and Its Applications (Springer Texts in Statistics) , 2005 .

[24]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[25]  Andrei Z. Broder,et al.  Estimating rates of rare events at multiple resolutions , 2007, KDD '07.

[26]  Weiguo Fan,et al.  Learning to advertise , 2006, SIGIR.

[27]  Samir Khuller,et al.  Online allocation of display advertisements subject to advanced sales contracts , 2009, KDD Workshop on Data Mining and Audience Intelligence for Advertising.

[28]  Sergei Vassilvitskii,et al.  Inventory Allocation for Online Graphical Display Advertising , 2010, ArXiv.

[29]  Nicole Immorlica,et al.  A combinatorial allocation mechanism with penalties for banner advertising , 2008, WWW.

[30]  Wei Li,et al.  Bid landscape forecasting in online ad exchange marketplace , 2011, KDD.

[31]  David M. Pennock,et al.  An Expressive Auction Design for Online Display Advertising , 2008, AAAI.