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ISBN: 978-972-8939-25-0 © 2010 IADISIn order to control the growth of reputation, the function also introduces a control factor T, where T is the sumof reputation value of every node in the network and evaluated when repuConverge(N) is called. The initialvalue of T is set to be 1.Function: updateReputation(n)Step 1: Denote by d the income degree of node n. Select one edge e pointing to nuniformly.Step 2: Update n’s reputation value based on the transaction data corresponding tothe selected edge e using ∆R( n)R( n)+ =, where ∆R(n) can be computed with the formulaTintroduced in section II.Function getNextNode(n) determines whether the random walk path will terminate. If it returns NULL, thepath terminates. If it returns a node n i which is pointed by n, the path continues and the destination of this jumpis n i . It is easy to see that the more the number of out edges is, the larger the jumping probability is. If a nodehas already been chosen in this loop before, the function should also return NULL.Function: getNextNode(n)Step 1: Denote by d the outcome degree of node n, and let n 1,…,n dbe the nodespointed by n. Select one element from the set {n 1,…,n d,NULL} uniformly.Step 2: if ( a node is selected and the node has not been chosen in this loopbefore )Step 3:return the nodeStep 4: else return NULL.Function repuConverge(N) is used to test whether the algorithm has converged and also to update thecontrol factor T. Clearly the increment of reputation tends to 0 because of the control factor T.Function: repuConverge(N)Step 1: if (the increment of every node’s reputation < δ)Step 2:return true;Step 3: else return false.Here δ is a constant threshold value.Clearly, the algorithm needs enough transaction data to induce the transaction social network. Therefore,when applying the algorithm into C2C electronic commerce reputation system, one had better use thealgorithm to modify reputation periodically. First, compute member reputation with the traditional algorithm.After a period, for example one month, compute member reputation using our algorithm based on thetransaction data of this period. Then one can average the reputation increment of this period computed by ouralgorithm and the reputation increment of this period computed by the traditional algorithm, and derive themodified member reputation value using this average value together with the reputation value at the beginningof this period.4. CONCLUSIONThis paper discusses a new issue of C2C e-commerce reputation systems: fairness. A novel memberreputation evaluating algorithm is proposed to enhance reputation system fairness. The algorithm employsthe random walk strategy on the transaction social network and effectively reduces the computing complexity.We expect online e-commerce service providers can adopt the algorithm.280