1. A computer implemented method for recommending items to consumers in a recommender system, comprising the steps of: receiving a stream of rating on items from consumers via a network; updating sequentially, by a computer, a singular value decomposition of a preference matrix using the stream of ratings, in which each rating is processed one at a time and independent of any other ratings, while receiving the ratings, and wherein the updating is a sequential rank-1 update of the singular value decomposition to produce a thin SVD, and wherein the preference matrix is X, and wherein the singular value decomposition factors the preference matrix X into two orthogonal matrices U and V, and a diagonal matrix S≐diag(s), such that USV T =X, and U T XV=S, where T represents the SVD transform, the elements of s are singular values and columns of U and V are left and right singular vectors, respectively, and further comprising: arranging non-negative elements on the diagonal of S in descending order, and a first non-zero element in each column of U is positive so that the thin SVD is unique; and discarding all but a predetermined number of the r largest singular values and the corresponding singular vectors so that a product of the resulting thinned matrices, U′S′V′≈X, is a best rank-r approximation of X in a least-squares sense, and a matrix U′ T X=S′V′ T ; predicting recommendations of particular items for a particular consumer based on the updated singular value decomposition while receiving the ratings and updating the singular value decomposition; and displaying the recommendations for the particular consumer on a graphical consumer interface via the network.