On Wed, Sep 4, 2013 at 3:06 PM, Sean Owen <[email protected]> wrote: > 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. > > Excellent! Straight to the point. That's the answer I was looking for. Also, thanks to Ted. He pretty much said the same thing.
> > On Wed, Sep 4, 2013 at 6:07 PM, Koobas <[email protected]> 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? > > >
