Koren, Volinsky: "CF for implicit feedback datasets"
On Tue, Jun 18, 2013 at 8:07 AM, Pat Ferrel <[email protected]> wrote: > They are on a lot of papers, which are you looking at? > > On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov <[email protected]> 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 <[email protected]> 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? > >
