Expressive Banner Ad Auctions and Model-Based Online Optimization for Clearing

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Expressive Banner Ad Auctions and Model-Based Online Optimization for Clearing

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Title: Expressive Banner Ad Auctions and Model-Based Online Optimization for Clearing
Author: Boutilier, Craig; Parkes, David C.; Sandholm, Tuomas; Walsh, William E.

Note: Order does not necessarily reflect citation order of authors.

Citation: Boutilier, Craig, David C. Parkes, Tuomas Sandholm, and William E. Walsh. 2008. Expressive banner ad auctions and model-based online optimization for clearing. In Proceedings of the Twenty-third AAAI Conference on Artificial Intelligence and the Twentieth Innovative Applications of Artificial Intelligence Conference: July 13-17, 2008, Chicago, Illinois, ed. American Association for Artificial Intelligence, 30-37. Menlo Park, Calif.: AAAI Press.
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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.
Published Version: http://portal.acm.org/citation.cfm?id=1620002
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/walsh08_banner.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4000342

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  • FAS Scholarly Articles [7106]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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