Recommenders and classifiers are very similar animals in general except for the training data.
You can view a recommender as an engine that invents a classifier for each user but it does this by using other user histories as training data. This means that there can be a lot of confusion when looking at either kind of beast at a micro level. On Thu, Aug 9, 2012 at 1:20 PM, ziad kamel <[email protected]> wrote: > Thanks again. > > A quick question , in recommendation , if we measure precision @ 1 , > how is that different from measuring precision in a classifier ? Does > that mean a recommender becomes a classifier at this case ? > > > > > On Thu, Aug 9, 2012 at 12:18 PM, Sean Owen <[email protected]> wrote: >> Yes, this is a definite weakness of the precision test as applied to >> recommenders. It is somewhat flawed; it is easy to apply and has some use. >> >> Any item the user has interacted with is significant. The less-preferred 84 >> still probably predict the most-preferred 16 to some extent. But you make a >> good point, the bottom of the list is of a different nature than the top, >> and that bias does harm the recommendations, making the test result less >> useful. >> >> This is not a big issue though if the precision@ number is quite small >> compared to the user pref list size. >> >> There's a stronger problem, that the user's pref list is not complete. A >> recommendation that's not in the list already may still be a good >> recommendation, in the abstract. But a precision test would count it as >> "wrong". >> >> nDCG is slightly better than precision but still has this fundamental >> problem. >> >> The "real" test is to make recommendations and then put them in front of >> users somehow and see how many are clicked or acted on. That's the best >> test but fairly impractical in most cases. >> >> On Thu, Aug 9, 2012 at 5:54 PM, ziad kamel <[email protected]> wrote: >> >>> I see, but we are removing the good recommendations and we are >>> assuming that the less preferred items by a user can predict his best >>> preferred. For example, a user that has 100 books , and preferred 16 >>> of them only while the rest are books he have read. By removing the 16 >>> we are left with 84 books that it seems won't be able to predict the >>> right set of 16 ? >>> >>> What are the recommended approaches to evaluate the results ? I assume >>> IR approach is one of them. >>> >>> Highly appreciating your help Sean . >>> >>> On Thu, Aug 9, 2012 at 11:45 AM, Sean Owen <[email protected]> wrote: >>> > Yes, or else those items would not be eligible for recommendation. And it >>> > would be like giving students the answers to a test before the test. >>> > >>> > On Thu, Aug 9, 2012 at 5:41 PM, ziad kamel <[email protected]> >>> wrote: >>> > >>> >> A related question please. >>> >> >>> >> Do Mahout remove the 16% good items before recommending and use the >>> >> 84% to predict the 16% ? >>> >> >>> >> >>>
