Applying Learning Algorithms to Preference Elicitation

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Applying Learning Algorithms to Preference Elicitation

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Title: Applying Learning Algorithms to Preference Elicitation
Author: Lahaie, Sébastien M.; Parkes, David C.

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Citation: Lahaie, Sébastien M. and David C. Parkes. 2004. Applying learning algorithms to preference elicitation. In EC'04: Proceedings of the 5th ACM Conference on Electronic Commerce: May 17-20, 2004, New York, New York, ed. Association for Computing Machinery (États-Unis), and Special Interest Group on Electronic Commerce, 180-188. New York, N.Y.: ACM Press.
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Abstract: We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms perform a polynomial number of queries. We also give conditions under which the resulting algorithms have polynomial communication. Our conversion procedure allows us to generate combinatorial auction protocols from learning algorithms for polynomials, monotone DNF, and linear-threshold functions. In particular, we obtain an algorithm that elicits XOR bids with polynomial communication.
Published Version: doi:10.1145/988772.988800
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/p214-lahaie.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:4054440

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  • FAS Scholarly Articles [7470]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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