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Adaptive Designs for Optimizing Online Advertisement Campaigns

  • Andrey PepelyshevEmail author
  • Yuri Staroselskiy
  • Anatoly Zhigljavsky
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

We investigate the problem of adaptive targeting for real-time bidding in online advertisement using independent advertisement exchanges. This is a problem of making decisions based on information extracted from large data sets related to previous experience. We describe an adaptive strategy for optimizing the click through rate which is a key criterion used by advertising platforms to measure the efficiency of an advertisement campaign. We also provide some results of statistical analysis of real data.

Keywords

User Agent Advertising Campaign Online Advertisement Sequential Splitting Click Through Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The paper is a result of collaboration of Crimtan, a provider of proprietary ad technology platforms and the 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 2016

Authors and Affiliations

  • Andrey Pepelyshev
    • 1
    Email author
  • Yuri Staroselskiy
    • 2
  • Anatoly Zhigljavsky
    • 3
  1. 1.Cardiff UniversityCardiffUK
  2. 2.Crimtan, 1 Castle LaneLondonUK
  3. 3.Lobachevskii State University of Nizhnii NovgorodNizhny NovgorodRussia

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