Sounds good to me. -s Am 06.08.2014 17:15 schrieb "Dmitriy Lyubimov" <[email protected]>:
> what i mean here i probably need to refactor it a little so that there's > part of algorithm that accepts co-occurrence input directly and which is > somewhat decoupled from the part that accepts u x item input and does > downsampling and co-occurrence construction. So i could do some > customization of my own to co-occurrence construction. Would that be > reasonable if i do that? > > > On Wed, Aug 6, 2014 at 5:12 PM, Dmitriy Lyubimov <[email protected]> > wrote: > > > Asking because i am considering pulling this implementation but for some > > (mostly political) reasons people want to try different things here. > > > > I may also have to start with a different way of constructing > > co-occurrences, and may do a few optimizations there (i.e. priority queue > > queing/enqueing does twice the work it really needs to do etc.) > > > > > > > > > > On Wed, Aug 6, 2014 at 5:05 PM, Sebastian Schelter < > > [email protected]> wrote: > > > >> I chose against porting all the similarity measures to the dsl version > of > >> the cooccurrence analysis for two reasons. First, adding the measures > in a > >> generalizable way makes the code superhard to read. Second, in > practice, I > >> have never seen something giving better results than llr. As Ted pointed > >> out, a lot of the foundations of using similarity measures comes from > >> wanting to predict ratings, which people never do in practice. I think > we > >> should restrict ourselves to approaches that work with implicit, > >> count-like > >> data. > >> > >> -s > >> Am 06.08.2014 16:58 schrieb "Ted Dunning" <[email protected]>: > >> > >> > On Wed, Aug 6, 2014 at 5:49 PM, Dmitriy Lyubimov <[email protected]> > >> > wrote: > >> > > >> > > On Wed, Aug 6, 2014 at 4:21 PM, Dmitriy Lyubimov <[email protected] > > > >> > > wrote: > >> > > > >> > > I suppose in that context LLR is considered a distance (higher > scores > >> > mean > >> > > > more `distant` items, co-occurring by chance only)? > >> > > > > >> > > > >> > > Self-correction on this one -- having given a quick look at llr > paper > >> > > again, it looks like it is actually a similarity (higher scores > >> meaning > >> > > more stable co-occurrences, i.e. it moves in the opposite direction > of > >> > > p-value if it had been a classic test > >> > > > >> > > >> > LLR is a classic test. It is essentially Pearson's chi^2 test without > >> the > >> > normal approximation. See my papers[1][2] introducing the test into > >> > computational linguistics (which ultimately brought it into all kinds > of > >> > fields including recommendations) and also references for the G^2 > >> test[3]. > >> > > >> > [1] http://www.aclweb.org/anthology/J93-1003 > >> > [2] http://arxiv.org/abs/1207.1847 > >> > [3] http://en.wikipedia.org/wiki/G-test > >> > > >> > > > > >
