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Experimental comparison of scalable online ad serving

Published: 24 August 2008 Publication History

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.

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Dart Motif: Ad Serving Features (2007), Doubleclick corporate site, http://www.dartmotif.com/formatsfeatures/formatsfeatures_adserving.asp
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Ad serving. (2007, February 22). In Wikipedia, The Free Encyclopedia. Retrieved 19:06, February 25, 2007, from http://en.wikipedia.org/w/index.php?title=Ad_serving&oldid=110102191
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Dwight, Allen, Merriman, et al. (1999), Method of delivery, targeting, and measuring advertising over networks, USPTO Patent Number 5,948,061
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Hardy, M. (2007), Topics in Actuarial Analysis: Bayes, Buhlmann and Beyond, Financial Engineering News, Iss. 45, http://www.fenews.com/fen45/topics_act_analysis/topics-in-act-analysis.htm
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Kitts, B. Laxminarayan, P., LeBlanc, B. and Meech, R. (2005). A formal analysis of search auctions including predictions on click fraud and bidding tactics. ACM Conference on E-Commerce - Workshop on Sponsored Search, Vancouver, UK. June 2005. Available October 15, 2005, at http://research.yahoo.com/workshops/ssa2005/sched.html
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Cited By

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  • (2014)Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at MicrosoftReal World Data Mining Applications10.1007/978-3-319-07812-0_10(181-201)Online publication date: 14-Nov-2014
  • (2013)The Making of a Large-Scale Online Ad Server: Practical Lessons Building One of the World's Largest Online Ad Servers2013 IEEE 13th International Conference on Data Mining Workshops10.1109/ICDMW.2013.159(172-179)Online publication date: Dec-2013

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    cover image ACM Conferences
    KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2008
    1116 pages
    ISBN:9781605581934
    DOI:10.1145/1401890
    • General Chair:
    • Ying Li,
    • Program Chairs:
    • Bing Liu,
    • Sunita Sarawagi
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 August 2008

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    1. ad serving
    2. online advertising

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    View all
    • (2014)Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at MicrosoftReal World Data Mining Applications10.1007/978-3-319-07812-0_10(181-201)Online publication date: 14-Nov-2014
    • (2013)The Making of a Large-Scale Online Ad Server: Practical Lessons Building One of the World's Largest Online Ad Servers2013 IEEE 13th International Conference on Data Mining Workshops10.1109/ICDMW.2013.159(172-179)Online publication date: Dec-2013

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