Hi, I'm new to Mahout and got confused how train and test data are split when evaluating recommenders.
I'm not sure whether data is split based on selecting partial item preferences, or selecting specific users(together with all their preferences). For example, train data accounts for 60%, and test data accounts for 40%. Does it indicates 40% total preferences will used for testing(regardless associated users)? In classification, all features associated with the users will be selected.. If partition criteria is based on preference, would it affect neighborhood similarity before computing recommended score? Cheers, Blade
