I'm suggesting the second one. In that way the test user's ratings in the training set will compose of both low and high rated items, that prevents the problem pointed out by Ahmet.
On Sat, Feb 16, 2013 at 11:19 PM, Sean Owen <[email protected]> wrote: > If you're suggesting that you hold out only high-rated items, and then > sample them, then that's what is done already in the code, except without > the sampling. The sampling doesn't buy anything that I can see. > > If you're suggesting holding out a random subset and then throwing away the > held-out items with low rating, then it's also the same idea, except you're > randomly throwing away some lower-rated data from both test and train. I > don't see what that helps either. > > > On Sat, Feb 16, 2013 at 9:41 PM, Tevfik Aytekin > <[email protected]>wrote: > >> What I mean is you can choose ratings randomly and try to recommend >> the ones above the threshold >> >>
