OK , in the precison when we reduce the size of sample to .1 or 0.05 ,
would the results be related when we check with all the data ? For
example, if we have data1 and data2 and test them using 0.1 and found
that data 1 is producing better results , would the same thing stand
when we check with all data?
IRStatistics stats = evaluator.evaluate(recommenderBuilder,
null, model, null, 10,
GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
0.05);
Many thanks
On Fri, Jan 25, 2013 at 12:26 PM, Sean Owen <[email protected]> wrote:
> No, it takes a fixed "at" value. You can modify it to do whatever you want.
> You will see it doesn't bother with users with little data, like <
> 2*at data points.
>
> On Fri, Jan 25, 2013 at 6:23 PM, Zia mel <[email protected]> wrote:
>> 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 !
