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 !
