Yeah, I think the idea of confidence is a bit different than what I am looking for using implicit factorization to do document clustering.
I basically need (r_ij - w_ih_j)^2 for all observed ratings and (0 - w_ih_j)^2 for all the unobserved ratings...Think about the document x word matrix where r_ij is the count that's observed, 0 are the word counts that are not in particular document. The broadcasted value of gram matrix w_i'wi or h_j'h_j will also count the r_ij those are observed...So I might be fine using the broadcasted gram matrix and use the linear term as \sum (-r_ijw_i) or \sum (-rijh_j)... I will think further but in the current implicit formulation with confidence, looks like I am really factorizing a 0/1 matrix with weights 1 + alpha*rating for . It's a bit different from LSA model. On Sun, Jul 26, 2015 at 12:34 AM, Sean Owen <so...@cloudera.com> wrote: > confidence = 1 + alpha * |rating| here (so, c1 means confidence - 1), > so alpha = 1 doesn't specially mean high confidence. The loss function > is computed over the whole input matrix, including all missing "0" > entries. These have a minimal confidence of 1 according to this > formula. alpha controls how much more confident you are in what the > entries that do exist in the input mean. So alpha = 1 is low-ish and > means you don't think the existence of ratings means a lot more than > their absence. > > I think the explicit case is similar, but not identical -- here. The > cost function for the explicit case is not the same, which is the more > substantial difference between the two. There, ratings aren't inputs > to a confidence value that becomes a weight in the loss function, > during this factorization of a 0/1 matrix. Instead the rating matrix > is the thing being factorized directly. > > On Sun, Jul 26, 2015 at 6:45 AM, Debasish Das <debasish.da...@gmail.com> > wrote: > > Hi, > > > > Implicit factorization is important for us since it drives recommendation > > when modeling user click/no-click and also topic modeling to handle 0 > counts > > in document x word matrices through NMF and Sparse Coding. > > > > I am a bit confused on this code: > > > > val c1 = alpha * math.abs(rating) > > if (rating > 0) ls.add(srcFactor, (c1 + 1.0)/c1, c1) > > > > When the alpha = 1.0 (high confidence) and rating is > 0 (true for word > > counts), why this formula does not become same as explicit formula: > > > > ls.add(srcFactor, rating, 1.0) > > > > For modeling document, I believe implicit Y'Y needs to stay but we need > > explicit ls.add(srcFactor, rating, 1.0) > > > > I am understanding confidence code further. Please let me know if the > idea > > of mapping implicit to handle 0 counts in document word matrix makes > sense. > > > > Thanks. > > Deb > > >
