Log-likelihood similarity is a bit of a force-fit of the concept of the LLR. It is basically a binarizing and sparsifying filter applied to cooccurrence counts.
As such, it is eminently suited to implementation using a matrix multiply. On Fri, Apr 12, 2013 at 8:35 AM, Pat Ferrel <p...@occamsmachete.com> wrote: > That looks like the best shortcut. It is one of the few places where the > rows of one and the columns of the other are seen together. Now I know why > you transpose the first input :-) > > But, I have begun to wonder whether it is the right thing to do for a > cross recommender because you are comparing a purchase vector to all view > vectors--like comparing Honey Crisp to Cameo (couldn't resist an apples to > apples joke). Co-occurrence makes sense but does cosine or log-likelihood? > Maybe... > > > On Apr 11, 2013, at 10:49 AM, Sebastian Schelter <s...@apache.org> wrote: > > > Do I have to create a SimilarityJob( matrixB, matrixA, similarityType > ) to get this or have I missed something already in Mahout? > > It could be worth to investigate whether MatrixMultiplicationJob could > be extended to compute similarities instead of dot products. > > Best, > Sebastian > >
