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 !
