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Memory-based collaborative filtering selects the top-k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them. The proposed method minimizes the similarity evaluation errorsdoi:10.1155/2013/847965 fatcat:vbb7ehanrzbgbeucvoigxbkt24