We create a support system for predicting end prices on eBay. The end price predictions are based on the item descriptions found in the item listings of eBay, and on some numerical item features. The system uses text mining and boosting algorithms from the field of machine learning. Our system substantially outperforms the naive method of predicting the category mean price. Moreover, interpretation of the model enables us to identify influential terms in the item descriptions and shows that the item description is more influential than the seller feedback rating, which was shown to be influential in earlier studies.

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Keywords boosting, eBay, electronic auctions, text mining
Persistent URL dx.doi.org/10.1016/j.dss.2007.11.004, hdl.handle.net/1765/13581
Series ERIM Top-Core Articles
Journal Decision Support Systems
van Heijst, D.P, Potharst, R, & van Wezel, M.C. (2008). A support system for predicting eBay end prices. Decision Support Systems, 44(4), 970–982. doi:10.1016/j.dss.2007.11.004