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From ziad kamel <ziad.kame...@gmail.com>
Subject Re: How good recommendations and precision works
Date Thu, 09 Aug 2012 20:20:39 GMT
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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