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In Electronic Commerce it is not easy for customers to find the best suitable goods as more and more information is placed on line. In order to provide information of high value a customized recommender system is required. One of the typical information retrieval techniques for recommendation systems in Electronic Commerce is collaborative filtering which is based on the ratings of other customers...
In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fact that they are similar customers, by properly converting data before performing clustering. The second...
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