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From Sean Owen <>
Subject Re: How good recommendations and precision works
Date Thu, 09 Aug 2012 17:18:10 GMT
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

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

nDCG is slightly better than precision but still has this fundamental

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 <> 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 <> 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 <>
> 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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