Focussing on rating error is also problematic in that it causes us to worry about being correct about the estimated ratings for items that will *never* be shown to a user.
In my mind, the only thing that matters in a practical system is the ordering of the top few items and the rough composition of the top few hundred items. Everything else makes no difference at all for real-world recommendations. On Thu, Aug 12, 2010 at 12:53 PM, Sean Owen <[email protected]> wrote: > I agree with your reading of what the Herlocker paper is saying. The > paper is focused on producing one estimated rating, not > recommendations. While those tasks are related -- recommendations are > those with the highest estimated ratings -- translating what's in > Herlocker directly to a recommendation algorithm is a significant > jump. >
