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Adaptive Targeting in Online Advertisement: Models Based on Relative Influence of Factors

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Machine Learning, Optimization, and Big Data (MOD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

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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 demand partners, these decisions are based on information extracted from big data sets containing records of previous impressions, clicks and subsequent purchases. We discuss several criteria which allow us to assess the significance of different factors on probabilities of clicks and conversions. We then devise simple strategies that are based on the use of the most influential factors and compare their performance with strategies that are much more computationally demanding. To make the numerical comparison, we use real data collected by Crimtan in the process of running several recent ad campaigns.

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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, Cardiff, UK

    Andrey Pepelyshev & Anatoly Zhigljavsky

  2. Crimtan, London, UK

    Yuri Staroselskiy & Roman Guchenko

  3. Lobachevskii State University of Nizhnii Novgorod, Nizhnii Novgorod, Russia

    Anatoly Zhigljavsky

  4. St. Petersburg State University, Saint Petersburg, Russia

    Roman Guchenko

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

Correspondence to Andrey Pepelyshev .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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Pepelyshev, A., Staroselskiy, Y., Zhigljavsky, A., Guchenko, R. (2016). Adaptive Targeting in Online Advertisement: Models Based on Relative Influence of Factors. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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