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 > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
