poster

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

Authors:

Suleyman Cetintas,

Datong Chen,

Luo Si,

Bin Shen,

Zhanibek DatbayevAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1217 - 1218

https://doi.org/10.1145/2009916.2010127

Published: 24 July 2011 Publication History

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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.

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Cited By

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Zhang XQin HChou MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Online resource allocation with non-stationary customersProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694537(59700-59730)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694537
Yuan HQian YBai M(2019)Efficient Mining of Event Periodicity in Data SeriesDatabase Systems for Advanced Applications10.1007/978-3-030-18576-3_8(124-139)Online publication date: 24-Apr-2019
https://doi.org/10.1007/978-3-030-18576-3_8
Mourelatos ETzagarakis M(2018)An investigation of factors affecting the visits of online crowdsourcing and labor platformsNetnomics10.1007/s11066-018-9128-z19:3(95-130)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11066-018-9128-z
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Forecasting counts of user visits for online display advertising with probabilistic latent class models
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Zhang XQin HChou MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Online resource allocation with non-stationary customersProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694537(59700-59730)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694537
Yuan HQian YBai M(2019)Efficient Mining of Event Periodicity in Data SeriesDatabase Systems for Advanced Applications10.1007/978-3-030-18576-3_8(124-139)Online publication date: 24-Apr-2019
https://doi.org/10.1007/978-3-030-18576-3_8
Mourelatos ETzagarakis M(2018)An investigation of factors affecting the visits of online crowdsourcing and labor platformsNetnomics10.1007/s11066-018-9128-z19:3(95-130)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11066-018-9128-z
Zhang JWang ZLi QZhang JLan YLi QSun XSingh SMarkovitch S(2017)Efficient delivery policy to minimize user traffic consumption in guaranteed advertisingProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298277(252-258)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298277
Wang CKalra ABorcea CChen YBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Viewability Prediction for Online Display AdsProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806536(413-422)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806536
Fischer ULehner W(2014)Transparent Forecasting Strategies in Database Management SystemsBusiness Intelligence10.1007/978-3-319-05461-2_5(150-181)Online publication date: 2014
https://doi.org/10.1007/978-3-319-05461-2_5
Cetintas SChen DSi L(2012)Forecasting user visits for online display advertisingInformation Retrieval10.1007/s10791-012-9201-416:3(369-390)Online publication date: 30-May-2012
https://doi.org/10.1007/s10791-012-9201-4

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Forecasting user visits for online display advertising

Online Display Advertising: Targeting and Obtrusiveness

Introduction to display advertising: a half-day tutorial

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