Abstract
Millions of people participate in online auctions on websites such as eBay. The data available in these public markets offer interesting opportunities to study Internet auctions. We explore techniques for identifying common bidding patterns on eBay using data from eBay video game console auctions. The analysis reveals that there are certain bidding behaviors that appear frequently in the data, some of which have been previously identified and others which are new. We propose new attributes of bidding engagements and rules for classifying strategies. In addition, we suggest economic motivations that might lead to the identified behaviors. We then apply a clustering algorithm to look at each bidder’s behavior across several engagements, and find a few natural clusters. Finally, we apply association rule analysis to the data and find cases of likely shill behavior, but find no cross-correlation with any particular bidding behavior.
This paper is an extended version of a paper presented at WEBKDD 2002[1].
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References
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© 2003 Springer-Verlag Berlin Heidelberg
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Shah, H.S., Joshi, N.R., Sureka, A., Wurman, P.R. (2003). Mining eBay: Bidding Strategies and Shill Detection. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds) WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. WebKDD 2002. Lecture Notes in Computer Science(), vol 2703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39663-5_2
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DOI: https://doi.org/10.1007/978-3-540-39663-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20304-9
Online ISBN: 978-3-540-39663-5
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