Abstract
Collaborative filtering is one of the most widely used method in personalized recommendations. The most important step of the method is obtaining the similarities among users by using ratings information so that system can predict user preferences. However, most similarity measures are not efficient enough in the face of cold start and data sparsity problem. To measure user similarity comprehensively and objectively, this paper introduces a segmented similarity measure model. This model not only calculates the similarity model based on the number of user ratings but also makes full use of user attribute similarity and item similarity to improve the accuracy of similarity. Experiments using two reals datasets show that the proposed method relieves cold start and data sparsity issues and improves the prediction accuracy and recommendation quality.
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