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Adaptive Targeting for Online Advertisement

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

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.

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Acknowledgement

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”.

Author information

Authors and Affiliations

  1. Cardiff University, Nizhnii Novgorod, Russia

    Andrey Pepelyshev & Anatoly Zhigljavsky

  2. Crimtan, London, UK

    Yuri Staroselskiy

  3. University of Nizhnii Novgorod, Nizhnii Novgorod, Russia

    Anatoly Zhigljavsky

Authors
  1. Andrey Pepelyshev
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  2. Yuri Staroselskiy
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  3. Anatoly Zhigljavsky
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Corresponding author

Correspondence to Andrey Pepelyshev .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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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. https://doi.org/10.1007/978-3-319-27926-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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