计算机科学
对抗制
比例(比率)
人工智能
计算机安全
质量(理念)
订单(交换)
数据科学
机器学习
深度学习
业务
地理
地图学
财务
认识论
哲学
作者
Brendan Kitts,Jing Zhang,Gang Wu,Wesley Brandi,Julien Beasley,Kieran Morrill,John Ettedgui,Sid Siddhartha,Hong Yuan,Feng Gao,Peter Azo,Raj Mahato
出处
期刊:Annals of information systems
日期:2015-01-01
被引量:14
标识
DOI:10.1007/978-3-319-07812-0_10
摘要
Microsoft adCenter is the third largest Search advertising platform in the United States behind Google and Yahoo, and services about 10 % of US traffic. At this scale of traffic approximately 1 billion events per hour, amounting to 2.3 billion ad dollars annually, need to be scored to determine if it is fraudulent or bot-generated [32, 37, 41]. In order to accomplish this, adCenter has developed arguably one of the largest data mining systems in the world to score traffic quality, and has employed them successfully over 5 years. The current paper describes the unique challenges posed by data mining at massive scale, the design choices and rationale behind the technologies to address the problem, and shows some examples and some quantitative results on the effectiveness of the system in combating click fraud.
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