research-article

Simple and Scalable Response Prediction for Display Advertising

Authors:

Olivier Chapelle,

Eren Manavoglu,

Romer RosalesAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 4

Article No.: 61, Pages 1 - 34

https://doi.org/10.1145/2532128

Published: 29 December 2014 Publication History

Abstract

Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article 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.

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Simple and Scalable Response Prediction for Display Advertising

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 4

Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

January 2015

390 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2699158

Editor:
Huan Liu
Arizona State University, USA

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Copyright © 2014 ACM.

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Publication History

Published: 29 December 2014

Accepted: 01 August 2013

Revised: 01 April 2013

Received: 01 December 2012

Published in TIST Volume 5, Issue 4

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