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.
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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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Pepelyshev, A., Staroselskiy, Y., Zhigljavsky, A. (2016). Adaptive Designs for Optimizing Online Advertisement Campaigns. In: Kunert, J., Müller, C., Atkinson, A. (eds) mODa 11 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-31266-8_23
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DOI: https://doi.org/10.1007/978-3-319-31266-8_23
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