Adaptive targeting for online advertisement

Pepelyshev, Andrey ORCID: https://orcid.org/0000-0001-5634-5559, Staroselskiy, Yuri and Zhigljavsky, Anatoly ORCID: https://orcid.org/0000-0003-0630-8279 2016. Adaptive targeting for online advertisement. Presented at: International Workshop on Machine Learning, Optimization and Big Data, Taormina, Italy, 21-23 July 2015. Published in: Pardalos, Panos, Pavone, Mario, Farinella, Giovanni Maria and Cutello, Vincenzo eds. Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.9432 Springer Verlag, pp. 240-251. 10.1007/978-3-319-27926-8_21

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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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Springer Verlag
ISBN:	9783319279251
ISSN:	0302-9743
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	16 December 2015
Last Modified:	30 Nov 2024 21:45
URI:	https://orca.cardiff.ac.uk/id/eprint/86818

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