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From Sean Owen <sro...@gmail.com>
Subject Re: The best evaluator for recommendations in binary data sets
Date Tue, 28 Jul 2009 13:52:09 GMT
No, really those types of evaluation do not apply to your case. Those
evaluate how closely the estimated preference values match real ones.
But in your case, you have no preference values (or they're implicitly
all '1.0' or something) so this is meaningless.

What you are likely interested in is something related but different
-- statistics like precision and recall. That is you are concerned
with whether the recommender will recommend a lot of items the user
would be associated to. For example maybe you take away three of the
user's items and see if the recommender recommends 3 of them back.

Look at GenericRecommenderIRStatsEvaluator instead. It can compute
precision and recall figures, which is more what you want.

On Tue, Jul 28, 2009 at 2:35 PM, Claudia Grieco<grieco@crmpa.unisa.it> wrote:
> Hi guys,
>
> I have created an user based recommender which operates on a binary  data
> set (an user has bought or not bought a product)
>
> I'm using BooleanTanimoto Coefficient, BooleanUserGenericUserBased and so
> on.
>
> Is using AverageAbsoluteDifferenceRecommenderEvaluator to evaluate the
> recommender a good idea?
>
>

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