Impossible to say. More data means a more reliable estimate all else equal. That's about it. On Jan 28, 2013 5:17 PM, "Zia mel" <[email protected]> wrote:
> Any thoughts of this ? > > On Sat, Jan 26, 2013 at 10:55 AM, Zia mel <[email protected]> wrote: > > 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 ! >
