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From Sean Owen <sro...@gmail.com>
Subject Re: How good recommendations and precision works
Date Thu, 09 Aug 2012 15:52:33 GMT
Hi Ziad, I did answer your last question on this list -- don't see this one
previously though.

The "relevant" items are chosen as those whose pref value exceed some given
threshold. The default threshold is the mean of all 100 pref values plus
one standard deviation. Assuming the prefs are about normally distributed
about the mean (a significant assumption), and because 84% of the data
should therefore fall below mean plus 1 standard deviation, that means you
pick about the top 16% (16 of 100) items as relevant.

Yes your interpretation of precision is correct.

On Thu, Aug 9, 2012 at 4:12 PM, ziad kamel <ziad.kamel25@gmail.com> wrote:

> Hi , I asked this question few months ago with no answer. Hopefully
> someone can help .
>
> When not using a threshold, the default is to use average ratings plus
> one standard deviation which equals to 16%. Assume that a user have
> 100 items. Does that mean that his good recommendations are the top 16
> items ? In case we use precision at 5 , we going to select  only top 5
> items from the 100.  So is the precison going to be how many among the
> 16 items are in the 5 items ? Assume that we get 4 from the 16 in list
> of 5 , the precision will be 80% ?
>
> IRStatistics stats = evaluator.evaluate(recommenderBuilder, null,
> model, null, 5,
> GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
>
> Thanks !
>

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