Title: Learning revenue-maximizing orderings in sequential auctions
Authors: Verwer, Sicco
Zhang, Yingqian #
Issue Date: 2011
Host Document: Proceedings of the 23rd Benelux Conference on Artificial Intelligence pages:320-327
Conference: Benelux Conference on Artificial Intelligence edition:23 location:Ghent, Belgium date:3-4 November 2011
Abstract: When multiple items are auctioned sequentially, the ordering of auctions plays an important role in the total revenue collected by the auctioneer. This is true especially with budget constrained bidders and the presence of complementarities among items. In such sequential auction settings, it is difficult to develop efficient algorithms for finding an optimal sequence of items. However, when historical data are available it is possible to learn good orderings that increase the revenue of the auctioneer. In this work, we show
how such a learning model can be built based on previous auctions using regression trees. We provide a greedy method that finds a good sequence for a new set of items given the learned model. We design several experiment scenarios and test the performance of the proposed learning method. The experimental results are promising: they show that good orderings can be found quickly.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
# (joint) last author

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