On Wed, Sep 4, 2013 at 10:07 AM, 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?
if it is a coincidence matrix in a sense that there are just 0's and 1's no it shouldn't (imo). However, if there's a case of no-observations then things are a little bit more complicated (in a sense that preference is still 0 and 1 but there're confidence weights. Determining weights (no-observation weight vs. degree of consumption) is usually advised to be determined via (cross)validation. However at this point Mahout does not support crossvalidation of those parameters, so usually people use some guesswork (see Zhou-Koren-Volinsky paper about implicit feedback datasets). > > 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? SVD does not take weights so it cannot ignore or weigh out a non-observation, which is why it is not well suited for matrix completion problem per se.
