Revenue Prediction in Budget-constrained Sequential Auctions with Complementarities

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 that optimizes the revenue of the auctioneer. However, when historical data are available, it is possible to learn a model in order to predict the outcome of a given sequence. In this work, we show how to construct such a model, and provide methods that finds a good sequence for a new set of items given the learned model. We develop an auction simulator and design several experiment settings to test the performance of the proposed methods.