What I mean is you can choose ratings randomly and try to recommend the ones above the threshold
On Sat, Feb 16, 2013 at 10:32 PM, Sean Owen <sro...@gmail.com> wrote: > Sure, if you were predicting ratings for one movie given a set of ratings > for that movie and the ratings for many other movies. That isn't what the > recommender problem is. Here, the problem is to list N movies most likely > to be top-rated. The precision-recall test is, in turn, a test of top N > results, not a test over prediction accuracy. We aren't talking about RMSE > here or even any particular means of generating top N recommendations. You > don't even have to predict ratings to make a top N list. > > > On Sat, Feb 16, 2013 at 9:28 PM, Tevfik Aytekin > <tevfik.ayte...@gmail.com>wrote: > >> No, rating prediction is clearly a supervised ML problem >> >> On Sat, Feb 16, 2013 at 10:15 PM, Sean Owen <sro...@gmail.com> wrote: >> > This is a good answer for evaluation of supervised ML, but, this is >> > unsupervised. Choosing randomly is choosing the 'right answers' randomly, >> > and that's plainly problematic. >> > >> > >> > On Sat, Feb 16, 2013 at 8:53 PM, Tevfik Aytekin < >> tevfik.ayte...@gmail.com>wrote: >> > >> >> I think, it is better to choose ratings of the test user in a random >> >> fashion. >> >> >> >> On Sat, Feb 16, 2013 at 9:37 PM, Sean Owen <sro...@gmail.com> wrote: >> >> > Yes. But: the test sample is small. Using 40% of your data to test is >> >> > probably quite too much. >> >> > >> >> > My point is that it may be the least-bad thing to do. What test are >> you >> >> > proposing instead, and why is it coherent with what you're testing? >> >> > >> >> >>
