Interesting. Using
IRStatistics stats = evaluator.evaluate(recommenderBuilder,
null, model, null, 5,
GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
1.0);
Can it be adjusted to each user ? In other words, is there a way to
select a threshold instead of using 5 ? mm Something like selecting y
set , each set have a min of z user ?
On Fri, Jan 25, 2013 at 12:09 PM, Sean Owen <[email protected]> wrote:
> The way I do it is to set x different for each user, to the number of
> items in the user's test set -- you ask for x recommendations.
> This makes precision == recall, note. It dodges this problem though.
>
> Otherwise, if you fix x, the condition you need is stronger, really:
> each user needs >= x *test set* items in addition to training set
> items to make this test fair.
>
>
> On Fri, Jan 25, 2013 at 4:10 PM, Zia mel <[email protected]> wrote:
>> When selecting precision at x let's say 5 , should I check that all
>> users have 5 items or more? For example, if a user have 3 items and
>> they were removed as top items, then how can the recommender suggest
>> items since there are no items to learn from?
>> Thanks !
