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 >
