Hi all, currently I am implementing a recommender based on a boolean pref data model. I have to evaluate this recommender and I have tried to use the precision and recall measurements Mahout provides. In Mahout-in-Action it says:
"There isn’t even a notion of relative preference on which to select a subset of good items. The best the test can do is randomly select some preferred items as the good ones." Does this mean the notion of what is good is really just random? Do you maybe have another idea what I can use to evaluate my recommender? Or do I have to manually define what is good to test the recommender? This would be very hard because of the large data set I use... Thanks a lot and greetings, Janina
