skip to main content
10.1145/2339530.2339719acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Factoring past exposure in display advertising targeting

Published: 12 August 2012 Publication History

Abstract

Online advertising is becoming more and more performance oriented where the decision to show an advertisement to a user is made based on the user's propensity to respond to the ad in a positive manner, (e.g., purchasing a product, subscribing to an email list). The user response depends on how well the ad campaign matches to the user's interest, as well as the amount of user's past exposure to the campaign - a factor shown to be impactful in controlled experimental studies. Past exposure builds brand-awareness and familiarity with the user, which in turn leads to a higher propensity of the user to buy/convert on the ad impression. In this paper we propose a model of the user response to an ad campaign as a function of both the interest match and the past exposure, where the interest match is estimated using historical search/browse activities of the user.
The goal of this paper is two-fold. First, we demonstrate the role played by the user interest and the past exposure in modeling user response by jointly estimating the parameters of these factors. We test this response model over hundreds of real ad campaigns. Second, we use the findings from this joint model to identify more relevant target users for ad campaigns. In particular, we show that on real advertising data this model combines past exposure together with the user profile to identify better target users over the conventional targeting models.

Supplementary Material

JPG File (306_w_talk_4.jpg)
MP4 File (306_w_talk_4.mp4)

References

[1]
Zoë Abrams and Erik Vee. Personalized ad delivery when ads fatigue: an approximation algorithm. WINE'07, pages 535--540, Berlin, Heidelberg, 2007. Springer-Verlag.
[2]
N. Archak, V. S Mirrokni, and S. Muthukrishnan. Mining advertiser-specific user behavior using adfactors. In WWW, pages 31--40, 2010.
[3]
Abraham Bagherjeiran, Andrew O. Hatch, and Advait Ratnaparkhi. Ranking for the conversion funnel. In SIGIR, 2010.
[4]
Abraham Bagherjeiran, Andrew O. Hatch, Advait Ratnaparkhi, and Rajesh Parekh. Large-scale customized models for advertisers. In ICDMW, 2010.
[5]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3(4-5):993--1022, 2003.
[6]
Ye Chen, Michael Kapralov, Dmitry Pavlov, and John F. Canny. Factor modeling for advertisement targeting. In NIPS, 2009.
[7]
Ye Chen, Dmitry Pavlov, and John F. Canny. Large-scale behavioral targeting. In KDD, 2009.
[8]
Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51:107--113, January 2008.
[9]
Xiang Fang, Surendra Singh, and Rohini Alhuwalia. An examination of different explanations for the mere exposure effect. Journal of consumer research, 34(1):97--103, 2007.
[10]
Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. Web-scale bayesian click-through rate prediction for sponsored search advertising in bing search engine. In ICML, 2010.
[11]
J. A. Hanley. Receiver Operating Characteristic (ROC) Curves. John Wiley & Sons, Ltd, 2005.
[12]
Wei Li, Xuerui Wang, Ruofei Zhang, Ying Cui, Yun Jiang, and Zheng Chen. Exploitation and exploration in a performance based contextual advertising system. In KDD, 2010.
[13]
Puneet Manchanda, Jean-Pierre Dube, Khim Yong Goh, and Pradeep K. Chintagunta. The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1):98--108, 2006.
[14]
Andrew Kachites McCallum. Mallet: A machine learning for language toolkit. http://www.cs.umass.edu/~mccallum/mallet, 2002.
[15]
Foster Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, and Alan Murray. Audience selection for on-line brand advertising: Privacy-friendly social network targeting. In KDD, 2009.
[16]
Navdeep Sahni. Effect of temporal spacing between advertising exposures: Evidence from an online field experiment. To appear.
[17]
Xiaoxiao Shi, Kevin L. Chang, Vijay K. Narayanan, Vanja Josifovski, and Alex Smola. A compression framework for user profile generation. In SIGIR Workshop on Feature Generation and Selection for Information Retrieval, 2010.
[18]
Ramakrishnan Srikant, Sugato Basu, Ni Wang, and Daryl Pregibon. User browsing models: Relevance versus examination. In KDD, 2010.
[19]
Jun Yan, Ning Liu, Gang Wang, Wen Zhang, Jianchang Mao, and Rong Jin. How much can behavioral targeting help online advertising? In WWW, 2009.

Cited By

View all
  • (2023)Workshop on Learning and Evaluating Recommendations with Impressions (LERI)Proceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608756(1248-1251)Online publication date: 14-Sep-2023
  • (2014)Modeling impression discounting in large-scale recommender systemsProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623356(1837-1846)Online publication date: 24-Aug-2014
  • (2014)A twitter recruitment intelligent system: association rule mining for smoking cessationSocial Network Analysis and Mining10.1007/s13278-014-0212-64:1Online publication date: 12-Aug-2014
  • Show More Cited By

Index Terms

  1. Factoring past exposure in display advertising targeting

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
      August 2012
      1616 pages
      ISBN:9781450314626
      DOI:10.1145/2339530
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 August 2012

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. advertising
      2. latent factors
      3. targeting
      4. user modeling

      Qualifiers

      • Research-article

      Conference

      KDD '12
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

      Upcoming Conference

      KDD '25

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)3
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 20 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)Workshop on Learning and Evaluating Recommendations with Impressions (LERI)Proceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608756(1248-1251)Online publication date: 14-Sep-2023
      • (2014)Modeling impression discounting in large-scale recommender systemsProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623356(1837-1846)Online publication date: 24-Aug-2014
      • (2014)A twitter recruitment intelligent system: association rule mining for smoking cessationSocial Network Analysis and Mining10.1007/s13278-014-0212-64:1Online publication date: 12-Aug-2014
      • (2013)A Twitter-based smoking cessation recruitment systemProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/2492517.2500228(854-861)Online publication date: 25-Aug-2013

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media