Experimental comparison of scalable online ad serving

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Experimental comparison of scalable online ad serving

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International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Las Vegas, Nevada, USA

SESSION: Industrial papers table of contents

Pages 1008-1015

Year of Publication: 2008

ISBN:978-1-60558-193-4

Authors

Gang Wu	Microsoft, Redmond, WA, USA
Brendan Kitts	Microsoft, Redmond, WA, USA

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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ABSTRACT

Online Ad Servers attempt to find best ads to serve for a given triggering user event. The performance of ads may be measured in several ways. We suggest a formulation in which the ad network tries to maximize revenue subject to relevance constraints. We describe several algorithms for ad selection and review their complexity. We tested these algorithms using Microsoft ad network from October 1 2006 to February 8 2007. Over 3 billion impressions, 8 million combinations of triggers with ads, and a number of algorithms were tested over this period. We discover curious differences between ad-servers aimed at revenue versus clickthrough rate.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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