Sean I think it is still a supervised learning problem in that there is a labelled training data set and an unlabeled test data set.
Learning a ranking doesn't change the basic dichotomy between supervised and unsupervised. It just changes the desired figure of merit. Sent from my iPhone On Feb 16, 2013, at 1:32 PM, Sean Owen <[email protected]> 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 > <[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? >>>>> >>>> >>
