Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval 2011
DOI: 10.1145/2009916.2010127
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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 his… Show more

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Cited by 6 publications
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“…In this work, we assume the user type distribution D is known in advance and the types of users are sampled from this distribution, while in other works, such as (Cetintas, Chen, and Si 2013;Cetintas et al 2011), authors study how to estimate user traffic in a particular period. Another line of works concern the pricing mechanism and revenue maximization in GD (Bharadwaj et al 2010;Radovanovic and Heavlin 2012).…”
Section: Related Workmentioning
confidence: 99%
“…In this work, we assume the user type distribution D is known in advance and the types of users are sampled from this distribution, while in other works, such as (Cetintas, Chen, and Si 2013;Cetintas et al 2011), authors study how to estimate user traffic in a particular period. Another line of works concern the pricing mechanism and revenue maximization in GD (Bharadwaj et al 2010;Radovanovic and Heavlin 2012).…”
Section: Related Workmentioning
confidence: 99%