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A Simple and scalable response prediction for display advertising
by
Olivier Chapelle
,
Eren Manavoglu Microsoft
,
Romer Rosales Linkedin
| Citations: | 9 - 4 self |
BibTeX
@MISC{Chapelle_asimple,
author = {Olivier Chapelle and Eren Manavoglu Microsoft and Romer Rosales Linkedin},
title = {A Simple and scalable response prediction for display advertising},
year = {}
}
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Abstract
Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this paper a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: it is easy to implement and deploy; it is highly scalable (we have trained it on terabytes of data); and it provides models with state-of-the-art accuracy.
