I tried to find more details about the boolean preferences but couldn't find any. Did you discover this idea or it has been known and used before?
On Fri, Jan 25, 2013 at 8:30 AM, Koobas <koo...@gmail.com> wrote: > Great suggestion! > Will do. > > > On Fri, Jan 25, 2013 at 1:10 AM, Sean Owen <sro...@gmail.com> wrote: > >> Why not test both the original and pruned data set? The low-rating >> data may still help, even when the rating is forgotten. >> I would not base the decision just on whether you can make >> recommendations to N users but the quality of recommendations overall. >> >> In this particular data set, which is rich and un-noisy, the ratings >> are probably valuable information and I imagine you will do better >> with any approach that doesn't drop them. >> >> On Fri, Jan 25, 2013 at 2:19 AM, Koobas <koo...@gmail.com> wrote: >> > They use a boolean recommender on the 10M MovieLens data >> > with negative ratings removed (including only 3 stars or more). >> > I wonder if this is a valid approach, as opposed to not removing >> anything. >> > >> > I actually went through the exercise of removing negative ratings from >> the >> > 10M MovieLens set, >> > and made the following observations: >> > >> > - It removes about 17% of all ratings, >> > - 15 users disappear (out of 70,000), >> > - 79 movies disappear (out of 10,000). >> > >> > So, it does not seem to hurt the overall exercise. >> > Reasonably small fraction of ratings is gone. >> > We will not recommend movies to a dozen users, who did not line anything. >> > We will not be recommending movies which nobody liked. >> > >> > I would definitely appreciate some comments about that approach. >>
