Adaptive Targeting for Online Advertisement

  • Andrey PepelyshevEmail author
  • Yuri Staroselskiy
  • Anatoly Zhigljavsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9432)


We consider the problem of adaptive targeting for real-time bidding for internet advertisement. This problem involves making fast decisions on whether to show a given ad to a particular user. For intelligent platforms, these decisions are based on information extracted from big data sets containing records of previous impressions, clicks and subsequent purchases. We discuss several strategies for maximizing the click through rate, which is often the main criteria of measuring the success of an advertisement campaign. In the second part of the paper, we provide some results of statistical analysis of real data.


Online advertisement Real-time bidding Adaptive targeting Big data Click through rate 



The paper is a result of collaboration of Crimtan, a provider of proprietary ad technology platform and University of Cardiff. Research of the third author was supported by the Russian Science Foundation, project No. 15-11-30022 “Global optimization, supercomputing computations, and application”.


  1. 1.
    Abello, J., Pardalos, P.M., Resende, M.G. (eds.): Handbook of Massive Data Sets, vol. 4. Springer Science and Business Media, New York (2002)zbMATHGoogle Scholar
  2. 2.
    Aly, M., Hatch, A., Josifovski, V., Narayanan, V.K.: Web-scale user modeling for targeting. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 3–12. ACM (2012)Google Scholar
  3. 3.
    Chakraborty, T., Even-Dar, E., Guha, S., Mansour, Y., Muthukrishnan, S.: Selective call out and real time bidding. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 145–157. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  4. 4.
    Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: selling billions of dollars worth of keywords. Am. Econ. Rev. 97(1), 242–259 (2007)CrossRefGoogle Scholar
  5. 5.
    eMarketer: US programmatic ad spend tops \({\$}\)10 Billion this year, to double by 2016 (2014).
  6. 6.
    Evans, D.S.: The online advertising industry: economics, evolution, and privacy. J. Econ. Perspect. 23(3), 37–60 (2009)CrossRefGoogle Scholar
  7. 7.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Jansen, B.J., Mullen, T.: Sponsored search: an overview of the concept, history, and technology. Int. J. Electr. Bus. 6(2), 114–131 (2008)CrossRefGoogle Scholar
  9. 9.
    Google: the arrival of real-time bidding. Technical report (2011)Google Scholar
  10. 10.
    de Leeuw, J., Mair, P.: Multidimensional scaling using majorization: SMACOF in R. SMACOF. J. Stat. Softw. 31(3), 1–30 (2009)CrossRefGoogle Scholar
  11. 11.
    McMahan, H.B.: Follow-the-regularized-leader and mirror descent: equivalence theorems and L1 regularization. In: International Conference on Artificial Intelligence and Statistics, pp. 525–533 (2011)Google Scholar
  12. 12.
    McMahan, H.B., Holt, G., Sculley, D., et al.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222–1230 (2013)Google Scholar
  13. 13.
    Muthukrishnan, S.: Ad exchanges: research issues. In: Leonardi, S. (ed.) WINE 2009. LNCS, vol. 5929, pp. 1–12. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  14. 14.
    Nicholls, S., Malins, A., Horner, M.: Real-time bidding in online advertising (2014).
  15. 15.
    Rendle, S.: Factorization machines. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 995–1000. IEEE (2010)Google Scholar
  16. 16.
    Tu, S., Lu, C.: Topic-based user segmentation for online advertising with latent dirichlet allocation. In: Cao, L., Zhong, J., Feng, Y. (eds.) ADMA 2010, Part II. LNCS, vol. 6441, pp. 259–269. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  17. 17.
    Wang, J., Yuan, S., Shen, X., Seljan, S.: Real-time bidding: a new frontier of computational advertising research. In: CIKM Tutorial (2013)Google Scholar
  18. 18.
    Witten, D.M., Tibshirani, R.: Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Comput. Stat. Data Anal. 55(1), 789–801 (2011)zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Yang, S., Ghose, A.: Analyzing the relationship between organic and sponsored search advertising: positive, negative or zero interdependence? Mark. Sci. 29(4), 602–623 (2010)CrossRefGoogle Scholar
  20. 20.
    Yuan, S., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: ADKDD (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrey Pepelyshev
    • 1
    Email author
  • Yuri Staroselskiy
    • 2
  • Anatoly Zhigljavsky
    • 1
    • 3
  1. 1.Cardiff UniversityNizhnii NovgorodRussia
  2. 2.CrimtanLondonUK
  3. 3.University of Nizhnii NovgorodNizhnii NovgorodRussia

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