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?
