1. A computer-implemented method in a recommendation system, comprising: sampling users in an initial usage matrix (R) to provide a reduced usage matrix (R′, R″) entries in the initial usage matrix represent an interest by the users in items, the users are represented by user vectors (ū R(i)) in one dimension of the initial usage matrix and the items are represented by item vectors ( v R(j)) in another dimension of the initial usage matrix, and the sampling results in the reduced usage matrix comprising a subset of the user vectors in the initial usage matrix R; factoring the reduced usage matrix using iterative matrix factorization to provide a user matrix (U′, U″) and an item matrix (V, V″) as factors of the reduced usage matrix; analytically determining a user matrix U as a factor of the initial usage matrix based on the item matrix and the initial usage matrix, the item matrix is also a factor of the initial usage matrix; and providing a recommendation to one of the users for one of the items using the user matrix and the item matrix which are factors of the initial usage matrix.