# Applying Learning Algorithms to Preference Elicitation

 Title: Applying Learning Algorithms to Preference Elicitation Author: Lahaie, Sébastien M.; Parkes, David C. Note: Order does not necessarily reflect citation order of authors. 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. Full Text & Related Files: Lahaie_Applying.pdf (151.2Kb; PDF) 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 Downloads of this work: