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 <ziad.kame...@gmail.com> 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 !
