Data driven design for online industrial auctions

Qing Chuan Ye, Jason Rhuggenaath, Yingqian Zhang (Corresponding author), Sicco E. Verwer, Michiel Jurgen Hilgeman

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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.
Original languageEnglish
Pages (from-to)675-691
Number of pages17
JournalAnnals of Mathematics and Artificial Intelligence
Issue number7
Early online date5 Jan 2021
Publication statusPublished - Jul 2021


  • Auction design
  • Machine learning
  • Optimization


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