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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)

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.

Keywords

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

Notes

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”.

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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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