Internet and Network Economics
Volume 3828 of the series Lecture Notes in Computer Science pp 34-45
Click Fraud Resistant Methods for Learning Click-Through Rates
- Nicole ImmorlicaAffiliated withMicrosoft Research
- , Kamal JainAffiliated withMicrosoft Research
- , Mohammad MahdianAffiliated withMicrosoft Research
- , Kunal TalwarAffiliated withMicrosoft Research
Abstract
In pay-per-click online advertising systems like Google, Overture, or MSN, advertisers are charged for their ads only when a user clicks on the ad. While these systems have many advantages over other methods of selling online ads, they suffer from one major drawback. They are highly susceptible to a particular style of fraudulent attack called click fraud. Click fraud happens when an advertiser or service provider generates clicks on an ad with the sole intent of increasing the payment of the advertiser. Leaders in the pay-per-click marketplace have identified click fraud as the most significant threat to their business model. We demonstrate that a particular class of learning algorithms, called click-based algorithms, are resistant to click fraud in some sense. We focus on a simple situation in which there is just one ad slot, and show that fraudulent clicks can not increase the expected payment per impression by more than o(1) in a click-based algorithm. Conversely, we show that other common learning algorithms are vulnerable to fraudulent attacks.
- Title
- Click Fraud Resistant Methods for Learning Click-Through Rates
- Book Title
- Internet and Network Economics
- Book Subtitle
- First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005. Proceedings
- Pages
- pp 34-45
- Copyright
- 2005
- DOI
- 10.1007/11600930_5
- Print ISBN
- 978-3-540-30900-0
- Online ISBN
- 978-3-540-32293-1
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- 3828
- Series ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Topics
- Industry Sectors
- eBook Packages
- Editors
-
- Xiaotie Deng (16)
- Yinyu Ye (17)
- Editor Affiliations
-
- 16. Department of Computer Science, City University of Hong Kong
- 17. Stanford University
- Authors
-
- Nicole Immorlica (18)
- Kamal Jain (18)
- Mohammad Mahdian (18)
- Kunal Talwar (18)
- Author Affiliations
-
- 18. Microsoft Research, Redmond, WA, USA
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