Behavioural Targeting in On-Line Advertising: An Empirical Study

  • Joanna Jaworska
  • Marcin Sydow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5175)

Abstract

On-line behavioural targeting is a dynamically evolving area of web mining concerning the applications of data analysis of on-line users’ behaviour and machine learning in optimising web on-line advertising and constitutes a problem of high importance and complexity.

The paper reports on experimental work concerning testing various benchmark machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting.

Our final goal is to build a system which automatically learns and subsequently decides which on-line advertisements to present to a user visiting a web page, based on his previous recorded behaviour, in order to maximise the revenue of the ad-network and the web site hosting the ad, and, at the same time, to minimise the user’s annoyance caused by potentially inappropriate advertisements.

We present a general adaptive model which makes it possible to test various machine-learning algorithms in a plug-in mode and its implementation.

We also report our experimental work concerning comparison of the performance of various machine-learning algorithms and data preprocessing techniques on a real dataset.

The performance of our experiments is evaluated by some objective metrics such as the click-through rate (CTR) or related.

Our experimental results clearly indicate that the presented adaptive system can significantly increase the CTR metric by 40% for some settings.

All the experiments are performed on a real industrial dataset concerning on-line ads emitted in the Polish Web during a 1-month period in 2007.

Up to the authors’ best knowledge this is the first and largest evaluation made on this kind of real industrial data in Poland, at the time of writing.

Keywords

behavioural targeting on-line advertising machine learning experimentation click-through rate user profile 

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References

  1. 1.
    http://www.facebook.com/business/?beacon (accessed February 19, 2008)
  2. 2.
    Weka software toolkit, http://www.cs.waikato.ac.nz/ml/weka/
  3. 3.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press / Addison-Wesley (1999)Google Scholar
  4. 4.
    Dyson, E.: Wall street journal (February 11, 2008), http://online.wsj.com
  5. 5.
    Higgs, B., Ringer, A.C.: Trends in consumer segmentation (2007) (accessed February 19, 2008), http://www.anzmac07.otago.ac.nz/anzmacCD/papers/Higgs1.pdf
  6. 6.
    Hu, J., Zeng, H.-J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web, pp. 151–160. ACM Press, New York (2007)CrossRefGoogle Scholar
  7. 7.
    Hubeman, B.A., Wu, F.: Economics of attention: maximizing user value in information-rich environments (August 2007)Google Scholar
  8. 8.
    Lacerda, A., Cristo, M., Gonçalves, M.A., Fan, W., Ziviani, N., Ribeiro-Neto, B.: Learning to advertise. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 549–556. ACM Press, New York (2006)CrossRefGoogle Scholar
  9. 9.
    Leenes, R.E.: Do you know me? decomposing identifiability. Tilburg University Legal Studies Working Paper No. 001/2008 (January 2008)Google Scholar
  10. 10.
    Ng, V., Mok, K.-H.: An intelligent agent for web advertisements. In: CODAS 2001: Proceedings of the Third International Symposium on Cooperative Database Systems for Advanced Applications, Washington, DC, USA, p. 102. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  11. 11.
    Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web, pp. 521–530. ACM Press, New York (2007)CrossRefGoogle Scholar
  12. 12.
    Smith, S.: Behavioral targeting could change the game (January 23, 2007) (accessed February 19, 2008) http://www.econtentmag.com/Articles/ArticleReader.aspx?ArticleID=18964
  13. 13.
    Tene, O.: What google knows? privacy and internet search engines (October 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Joanna Jaworska
    • 1
  • Marcin Sydow
    • 2
  1. 1.Gemius SAWarszawaPoland
  2. 2.Web Mining LabPolish-Japanese Institute of Information TechnologyWarszawaPoland

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