Scalable Online Ad Serving: Experimental Comparison of Simple Techniques

نویسندگان

  • Gang Wu
  • Brendan Kitts
چکیده

Online Ad Servers attempt to find the best ad to serve for a given triggering user event. The performance of the ad may be measured in several ways. We suggest a formulation in which the ad network is trying to maximize revenue subject to relevance constraints. We describe several simple algorithms for ad selection and review their complexity. We tested these algorithms using the Microsoft ad network from October 1 2006 to February 8 2007. Over 3 billion impressions, 9 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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تاریخ انتشار 2014