As the market of electronic commerce grows explosively, it becomes more and more important to provide the recommendation system which suggests the preferred items for consumers using the large-scale customers database. In this paper, we discuss the algorithms and their performances of the recommendation systems using the collaborative filtering in the case of the Netflix database: they are, 1) memory-based system (k-nearest neighbor using the correlation coefficients), 2) model-based system (matrix decomposition), and 3) the combination method. When the customer-item matrix is a sparse matrix like the Netflix database, the matrix decomposition method shows better performance than the k-nearrest neighbor; in addition, it is found that the combination method of the two methods provide a much better performance
