Generating realistic online auction data

  • Authors:

  • Sidney Tsang;Gillian Dobbie;Yun Sing Koh

  • Affiliations:

  • The University of Auckland, New Zealand;The University of Auckland, New Zealand;The University of Auckland, New Zealand

  • Venue:
  • AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
  • Year:
  • 2012

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Abstract

To combat online auction fraud, researchers have developed fraud detection and prevention methods. However, it is difficult to effectively evaluate these methods using commercial or synthetic auction data. For commercial data, it is not possible to accurately identify cases of fraud. For synthetic auction data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the TradeMe online auction site. We evaluate the generated data in two ways to show that it is similar to the TradeMe data. Evaluation of individual features show that correlation is greater than 0.9 for 8 of the 10 features, and evaluation using multiple features gives a median accuracy of 0.87.