Designing auction parameters for online industrial auctions is a complex problem due to highly heterogeneous items. Currently, online auctioneers rely heavily on their experts in auction design. The ability of predicting how well an auction will perform prior to the start comes in handy for auctioneers. If an item is expected to be a low-performing item, the auctioneer can take certain actions to influence the auction outcome. For instance, the starting selling price of the item can be modified, or the location where the item is displayed on the website can be changed to attract more attention. In this paper, we take a real-world industrial auction data set and investigate how we can improve upon the expert’s design using insights learned from data. More specifically, we first construct a classification model that predicts the expected performance of auctions. We propose a data driven auction design framework (called DDAD) that combines the expert’s knowledge with the learned prediction model, in order to find the best parameter values, i.e., starting price and display positions of the items, for a given new auction. The prediction model is evaluated, and the new design for several auctions is discussed and validated with the auction experts.

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In a sequential auction with multiple bidding agents, the problem of determining the ordering of the items to sell in order to maximize the expected revenue is highly challenging. The challenge is largely due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned regression models to integer linear programs (ILP), which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings. @en