Learning Revenue-Maximizing Orderings in Sequential Auctions
AbstractWhen multiple items are auctioned sequentially, the ordering of auctions plays an important role in the total revenue collected by the auctioneer. When historical data are available it is possible to learn good orderings that increase the revenue. In this work, we show how such a learning model can be built based on previous auctions using regression tree. 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.
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Bibliographic InfoPaper provided by International Institute of Social Studies of Erasmus University (ISS), The Hague in its series ISS Working Papers - General Series with number ERS-2011-020-LIS.
Date of creation: 24 Aug 2011
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optimization; learning; revenue maximization; sequential auctions; ordering of auctions;
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