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 <[email protected]>wrote: > No, rating prediction is clearly a supervised ML problem > > On Sat, Feb 16, 2013 at 10:15 PM, Sean Owen <[email protected]> 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 < > [email protected]>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 <[email protected]> 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? > >> > > >> >
