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From Sean Owen <sro...@gmail.com>
Subject Re: How good recommendations and precision works
Date Thu, 09 Aug 2012 20:37:38 GMT
Evaluating precision @ 1 is evaluating the 1st recommendation, whether it's
a good recommendation. It's like asking for the data point that a
classifier would classify as most probably in a certain class. That's not
the same as what a classifier is built to do, which is to decide whether
any given item is in a class or not. Those are obviously quite related
questions though.

On Thu, Aug 9, 2012 at 9:20 PM, ziad kamel <ziad.kamel25@gmail.com> 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 <srowen@gmail.com> 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 <ziad.kamel25@gmail.com>
> 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 <srowen@gmail.com> 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 <ziad.kamel25@gmail.com>
> >> wrote:
> >> >
> >> >> A related question please.
> >> >>
> >> >> Do Mahout remove the 16% good items before recommending and use the
> >> >> 84% to predict the 16% ?
> >> >>
> >> >>
> >>
>

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