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Adaptive targeting in online advertisement: models based on relative influence of factors

Pepelyshev, Andrey ORCID:, Staroselskiy, Yuri, Zhigljavsky, Anatoly ORCID: and Guchenko, Roman 2016. Adaptive targeting in online advertisement: models based on relative influence of factors. Presented at: International Workshop on Machine Learning, Optimization and Big Data, Volterra, Italy, 26-29 August 2016. Published in: Pardalos, P., Conca, P., Giuffrida, G. and Nicosia, G. eds. Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science , vol.10122 Springer, pp. 159-169. 10.1007/978-3-319-51469-7_13

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: Springer
ISSN: 0302-9743
Date of First Compliant Deposit: 12 May 2017
Date of Acceptance: 2 October 2016
Last Modified: 21 Oct 2022 07:35

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