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% ? >> >> >> >> >>
