Thanks for this clarification.Thanks for the new code as well, I am quite impressed by your responsiveness.
About your new code, in particular BookCrossingBooleanRecommender, I am a bit surprised that you use GenericUserBasedRecommander, and not GenericBooleanPrefsUserBasedRecommander, which seems to habe been designed for this purpose. Maybe is it just a mistake? 2010/3/11 Sean Owen <[email protected]> > 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. > > > > > > > > > > >
