Abstract
To responses to the current information "overload" problem widespread in e-commerce systems, a new approach, using the method of user clustering, node trust value analyzing and product evaluating is put forward to build an e-commerce trust community for e-commerce recommendation. According to some of the most trusted neighbors` evaluation information for goods, the recommendation model predicts the score of goods that the users have purchased, to recommend items which have a higher value score, to a customer. In the proposed recommending algorithm, the time effect of recommendation is taken into consideration to provide effective recommending services for users.
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