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On Applying Optimal Design of Experiments when Functional Observations Occur Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Adaptive Designs for Optimizing Online Advertisement Campaigns

Authors : Andrey Pepelyshev, Yuri Staroselskiy, Anatoly Zhigljavsky

Published in: mODa 11 - Advances in Model-Oriented Design and Analysis

Publisher: Springer International Publishing

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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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previous chapter Controlled Versus “Random” Experiments: A Principle
next chapter Interpolation and Extrapolation in Random Coefficient Regression Models: Optimal Design for Prediction
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Metadata
Title
Adaptive Designs for Optimizing Online Advertisement Campaigns
Authors
Andrey Pepelyshev
Yuri Staroselskiy
Anatoly Zhigljavsky
Copyright Year
2016
Book
mODa 11 - Advances in Model-Oriented Design and Analysis

Print ISBN: 978-3-319-31264-4

Electronic ISBN: 978-3-319-31266-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-31266-8_23

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