  Collaborative filtering tries to predict the ratings of a user over some
items based on opinions of other users with similar taste. The ratings are
usually given in the form of a sparse matrix, the goal being to find the
missing entries (i.e. ratings). Various approaches to collaborative filtering
exist, some of the most popular ones being the Singular Value Decomposition
(SVD) and K-means clustering. One of the challenges in the SVD approach is
finding a good initialization of the unknown ratings. A possible initialization
is suggested by [1]. In this paper we explain how K-means approach can be used
to achieve the further refinement of this initialization for SVD. We show that
our technique outperforms both initialization techniques used separately.
