On Tue, 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. I guess it makes sense for cancellations. i guess it should become pretty obvious from extensive crossvalidation search. > 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. > Or we just don't know exact prevailing reason for the returns. :) "did not fit" is "almost fit, give me similar", and "found on another sale event" means "I still like it, just not your price". However, if there's a consistent quality issue, it may turn bad enough to consider p=0. bottom line, it should become fairly obvious which reasoning prevails, thru validation. "Kill list" should probably be maintained for a whole lot of reasons, not just returns. E.g. something that was recently bought, may be one-a-lifetime purchase, or it may be replenishable with a certain period of repeatability (which could also be modelled). Does it makes sense? > > 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? > > >
