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SUMMARY:Ad-auctions: where game theory meets machine learning and computer
  science - Dr Peter Key\, Microsoft Research Cambridge
DTSTART:20130221T160000Z
DTEND:20130221T170000Z
UID:TALK42634@talks.cam.ac.uk
CONTACT:CCA
DESCRIPTION:Every time a user types a query into a search engine\, an auct
 ion is run to decide which ads (if any) to show in response\, and where to
  place them on the results page. When a user clicks on an ad\, the publish
 er is paid by the advertiser. This happens in real time\, thousands of tim
 es every second\, and generates billions of dollars in revenue. It is Goog
 le’s main source of income. Current auctions run by Google or Bing use a
  ranking algorithm which is a variant of a Generalised Second Price Auctio
 n. This e-commerce example is a rich area for research which lies at the i
 ntersection of mathematics\, economics and computer science. For example\,
  advertisers need to determine how to bid in the face of uncertainty\, a m
 achine learning problem\, while the auction designer wants to design a rob
 ust mechanism that balances the competing demands of users\, advertisers a
 nd publisher.\n\nWe describe such ad-auctions\, and illustrate how current
  systems have adapted insights taken from auction theory and optimisation 
 to design mechanisms that can be used at scale. Examples from live auction
 s are used for demonstration. Yet despite their ubiquity\, such repeated a
 uctions are not fully understood: the simplifications typically needed for
  analysis of single-shot auctions rarely hold in practice\, new methodolog
 y is required\, while new forms of advertising stretch the existing models
 . We give examples of some recent research.
LOCATION:MR3
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