To your point Ted, I was surprised to find that remove-from-cart actions predicted sales almost as well as purchases did but it also meant filtering from recs. We got the best scores treating them as purchases and not recommending them again. No one pried enough to get get bothered.
In this particular case I'm ingesting movie reviews, thumbs up or down. I'm trying to prime the pump for a cold start case of a media guide app with expert reviews but no users yet. Expert reviewers review everything so I don't think there will be much goodness in treating a thumbs down like a thumbs up in this particular case. Sean, are you suggesting that negative reviews might be modeled as a "0" rather than no value? Using the Mahout recommender this will only show up in filtering the negatives out of recs as Ted suggests, right? Since a "0" preference would mean, don't recommend, just as a preference of "1" would. This seems like a good approach but I may have missed something in your suggestion. In this case I'm not concerned with recommending to experts, I'm trying to make good recs to new users with few thumbs up or down by comparing them to experts with lots of thumbs up and down.The similarity metric will have new users with only a few preferences and will compare them to reviewers with many many more. I wonder if this implies a similarity metric that uses only common values (cooccurrence) rather than the usual log-likelihood? I guess it's easy to try both. Papers I've read on this subject. The first has an interesting discussion of using experts in CF. http://www.slideshare.net/xamat/the-science-and-the-magic-of-user-feedback-for-recommender-systems http://www.sis.pitt.edu/~hlee/paper/umap2009_LeeBrusilovsky.pdf On Jun 18, 2013, at 3:48 AM, Ted Dunning <ted.dunn...@gmail.com> wrote: I have found that in practice, don't-like is very close to like. That is, things that somebody doesn't like are very closely related to the things that they do like. Things that are quite distant wind up as don't-care, not don't-like. This makes most simple approaches to modeling polar preferences very dangerous. What I have usually done under the pressure of time is to consider like and don't-like to be equivalent synonyms and then maintain a kill list of items to not show. Works well pragmatically, but gives people hives when they hear of the details, especially if they actually believe humans act according to consistent philosophy. On Tue, Jun 18, 2013 at 9:13 AM, Sean Owen <sro...@gmail.com> wrote: > 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? >
