Yes the model has no room for literally negative input. I think that conceptually people do want negative input, and in this model, negative numbers really are the natural thing to express that.
You could give negative input a small positive weight. Or extend the definition of c so that it is merely small, not negative, when r is negative. But this was generally unsatisfactory. It has a logic, that even negative input is really a slightly positive association in the scheme of things, but the results were viewed as unintuitive. I ended up extending it to handle negative input more directly, such that negative input is read as evidence that p=0, instead of evidence that p=1. This works fine, and tidier than an ensemble (although that's a sound idea too). The change is quite small. Agree with the second point that learning weights is manual and difficult; that's unavoidable I think when you want to start adding different data types anyway. I also don't use M/R for searching parameter space since you may try a thousand combinations and each is a model build from scratch. I use a sample of data and run in-core. On Tue, Jun 18, 2013 at 2:30 AM, Dmitriy Lyubimov <dlie...@gmail.com> wrote: > (Kinda doing something very close. ) > > Koren-Volynsky paper on implicit feedback can be generalized to decompose > all input into preference (0 or 1) and confidence matrices (which is > essentually an observation weight matrix). > > If you did not get any observations, you encode it as (p=0,c=1) but if you > know that user did not like item, you can encode that observation with much > more confidence weight, something like (p=0, c=30) -- actually as high > confidence as a conversion in your case it seems. > > The problem with this is that you end up with quite a bunch of additional > parameters in your model to figure, i.e. confidence weights for each type > of action in the system. You can establish that thru extensive > crossvalidation search, which is initially quite expensive (even for > distributed machine cluster tech), but could be incrementally bail out much > sooner after previous good guess is already known. > > MR doesn't work well for this though since it requires A LOT of iterations. > > > > On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel <pat.fer...@gmail.com> wrote: > >> In the case where you know a user did not like an item, how should the >> information be treated in a recommender? Normally for retail >> recommendations you have an implicit 1 for a purchase and no value >> otherwise. But what if you knew the user did not like an item? Maybe you >> have records of "I want my money back for this junk" reactions. >> >> You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no >> value as usual means no preference? Some of the math here won't work though >> since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no >> preference implicitly = 0? >> >> Would it be better to treat the bad rating as a 1 and good as 2? This >> would be more like the old star rating method only we would know where the >> cutoff should be between a good review and bad (1.5) >> >> I suppose this could also be treated as another recommender in an ensemble >> where r = r_p - r_h, where r_h = predictions from "I hate this product" >> preferences? >> >> Has anyone found a good method?
