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Adaptive Targeting for Online Advertisement

Part of the Lecture Notes in Computer Science book series (LNISA,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

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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”.

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Correspondence to Andrey Pepelyshev .

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Pepelyshev, A., Staroselskiy, Y., Zhigljavsky, A. (2015). Adaptive Targeting for Online Advertisement. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham.

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