Obviously, you need to refer also to scores of other items as well. One handy stat is AUC whcih you can compute by averaging to get the probability that a relevant (viewed) item has a higher recommendation score than a non-relevant (not viewed) item.
On Sun, Aug 26, 2012 at 5:55 PM, Sean Owen <sro...@gmail.com> wrote: > There's another approach I've been playing with, which works when the > recommender produces some score for each rec, not just a ranked list. > You can train on data up to a certain point in time, then have the > recommender score the observations that really happened after that > point. Ideally it should produce a high score for things that really > were observed next. > >
