I agree with that explanation. Is it "why" it's unsupervised.. well I think of recommendation in the context of things like dimension reduction, which are just structure-finding exercises. Often the input has no positive or negative label (a click); everything is 'positive'. If you're predicting anything, it's not one target, but many targets, one per item, as if you have many small supervised problems.
Whatever that is called -- I was just saying that it's not a simple supervised problem, and so it's not necessarily true that the things you do when testing that kind of thing apply here. Viewed through the supervised lens, I suppose you could say that this process only ever predicts the positive class, and that's different. In fact it is not classifying given test examples at all... it's like it is telling you which of many classifiers (items) would be most likely to return the positive class On Sun, Feb 17, 2013 at 11:56 AM, Osman Başkaya <osman.bask...@computer.org>wrote: > I am sorry to extend the unsupervised/supervised discussion which is not > the main question here but I need to ask. > > Sean, I don't understand your last answer. Let's assume our rating scale is > from 1 to 5. We can say that those movies which a particular user rates as > 5 are relevant for him/her. 5 is just a number, we can use *relevance > threshold *like you did and we can follow the method described in Cremonesi > et al. Performance of Recommender Algorithms on Top-N Recommendation > Tasks<http://goo.gl/pejO7>( > *2. Testing Methodology - p.2*). > > Are you saying that this job is unsupervised since no user can rate all of > the movies. For this reason, we won't be sure that our predicted top-N list > contains no relevant item because it can be possible that our top-N > recommendation list has relevant movie(s) which hasn't rated by the user * > yet* as relevant. By using this evaluation procedure we miss them. > > In short, The following assumption can be problematic: > > We randomly select 1000 additional items unrated by > > user u. We may assume that most of them will not be > > of interest to user u. > > > Although bigger N values overcomes this problem mostly, still it does not > seem totally supervised. > > > On Sun, Feb 17, 2013 at 1:49 AM, Sean Owen <sro...@gmail.com> wrote: > > > The very question at hand is how to label the data as "relevant" and "not > > relevant" results. The question exists because this is not given, which > is > > why I would not call this a supervised problem. That may just be > semantics, > > but the point I wanted to make is that the reasons choosing a random > > training set are correct for a supervised learning problem are not > reasons > > to determine the labels randomly from among the given data. It is a good > > idea if you're doing, say, logistic regression. It's not the best way > here. > > This also seems to reflect the difference between whatever you want to > call > > this and your garden variety supervised learning problem. > > > > On Sat, Feb 16, 2013 at 11:15 PM, Ted Dunning <ted.dunn...@gmail.com> > > wrote: > > > > > 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. > > > > > > > > > -- > Osman Başkaya > Koc University > MS Student | Computer Science and Engineering >
