Abstract
We have developed a suite of algorithms to address two problems confronting reputation systems for large peer-to-peer markets: data sparseness and inaccurate feedback. To mitigate the effect of inaccurate feedback – particularly retaliatory negative feedback – we propose EM-trust, which uses a latent variable statistical model of the feedback process. To handle sparse data, we propose Bayesian versions of both EM-trust and the well-known Percent Positive Feedback system. Using a marketplace simulator, we demonstrate that these algorithms provide more accurate reputations than standard Percent Positive Feedback.
This research supported in part by the Digital Libraries Initiative under grant NSF CA98-17353.
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Traupman, J., Wilensky, R. (2006). Robust Reputations for Peer-to-Peer Marketplaces. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds) Trust Management. iTrust 2006. Lecture Notes in Computer Science, vol 3986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11755593_28
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DOI: https://doi.org/10.1007/11755593_28
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