Expressive banner ad auctions and model-based online optimization for clearing

  • Authors:

  • Craig Boutilier;David C. Parkes;Tuomas Sandholm;William E. Walsh

  • Affiliations:

  • Dept. of Computer Science, University of Toronto, Toronto, ON, Canada;SEAS, Harvard University, Cambridge, MA;Computer Science Dept., Carnegie Mellon University, Pittsburgh, PA;CombineNet, Inc., Pittsburgh, PA

  • Venue:
  • AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
  • Year:
  • 2008

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Abstract

We present the design of a banner advertising auction which is considerably more expressive than current designs. We describe a general model of expressive ad contract/bidding and an allocation model that can be executed in real time through the assignment of fractions of relevant ad channels to specific advertiser contracts. The uncertainty in channel supply and demand is addresscd by the formulation of a stochastic combinatorial optimization problem for channel allocation that is rerun periodically. We solve this in two different ways: fast deterministic optimization with respect to expectations; and a novel online sample-based stochastic optimization method-- that can be applied to continuous decision spaces--which exploits the deterministic optimization as a black box. Experiments demonstrate the importance of expressive bidding and the value of stochastic optimization.