The feature vectors? rows of X and Y? no, they definitely should not be normalized. It will change the approximation you so carefully built quite a lot.
As you say U and V are orthornormal in the SVD. But you still multiply all of them together with Sigma when making recs. (Or you embed Sigma in U and V.) So yes the singular values are used; they give proper weights to features. You can think of X and Y as being like that, with Sigma mixed in in some arbitrary way. Normalizing it would not be valid. On Wed, Sep 4, 2013 at 6:07 PM, Koobas <koo...@gmail.com> wrote: > In ALS the coincidence matrix is approximated by XY', > where X is user-feature, Y is item-feature. > Now, here is the question: > are/should the feature vectors be normalized before computing > recommendations? > > Now, what happens in the case of SVD? > The vectors are normal by definition. > Are singular values used at all, or just left and right singular vectors? >
