Abstract: In order to overcome the problem of traditional collaborative filtering algorithm faces severe challenge of sparse user ratings and real——time recommendation, a high efficient personalization recommendation algorithm based on rough set is proposed.The algorithm refine the user ratings data using dimensionality reduction, then uses the quality of approximation of classification to find the target users'neighbors.Thus the sparsity can be decreased and the accuracy of searching nearest neighbor can be improved.The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provide better recommendation results than traditional collaborative filtering algorithms.