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By Wen Chen Hu; Chung-Wei Lee; Weidong Kou

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S. enterprises plan to implement a wireless/mobile solution by 2003. jsp? htm Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Reputation and Trust 19 Chapter II Reputation and Trust Li Xiong, Georgia Institute of Technology, USA Ling Liu, Georgia Institute of Technology, USA Abstract This chapter introduces reputation systems as a means of facilitating trust and minimizing risks in m-commerce and e-commerce in general.

Acknowledgments We would like to thank the reviewer of this chapter and the editors of the book for their valuable comments. This work is partially supported by the National Science Foundation under an CISE ITR grant, an CISE CCR grant, a grant from DoE SciDAC. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or DoE. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc.

A simpler approach is to infer or compute the credibility value of a peer implicitly. We discuss two such credibility measures. The first one is to use a function of the trust value of a peer as its credibility factor recursively so feedback from trustworthy peers are considered more credible and thus weighted more than those from untrustworthy peers. We refer to the basic trust metric that uses the Trust Value of a peer recursively as its credibility Measure as PeerTrust TVM metric and it is defined as follows: I (u ) T (u ) = ∑ S (u, i) * T ( p(u, i)) i =1 I (u ) ∑ T ( p(u, i)) i =1 This solution is based on two assumptions.

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