You are right, you can't evaluate average error of predicted rating since there is no rating.
What you can do is do a sort of precision-recall test on the results. You take out some items from a user's set of associations, and see how many of them are recommended back. It's an imperfect test -- it's not necessarily true that those removed items are the best recommendations, but they're not bad probably. However it's better than nothing. There were some pretty interesting discussions on this very topic on mahout-user a few weeks ago, you might check the archives. If you update your code from SVN you'll see I just checked in an example of this for Book Crossing. Lastly I'll plug the book which has pretty good coverage of evaluation: http://manning.com/owen On Thu, Mar 11, 2010 at 11:02 AM, <[email protected]> wrote: >> You'll be willing to look at GenericBooleanPrefDataModel, >> GenericItemBasedRecommender, GenericBooleanPrefUserBasedRecommender >> and similarities supporting boolean preferences like Tanimoto and >> LogLikelihood. > > Thanks a lot, this is really helpful, I missed that. One problem with these > kind > of recommender based on implicit ratings (user x bought item y) is that there > are no evaluator for them. I have to say it is not even clear to me what such > an > evaluator should actually evaluate. > > Any references on document on the subject would be really appreciated. > > > > >
