SVDRecommender is really sensitive to the random number seed. AADRE gives about a 20% spread in its evaluations. (I have only tried AverageAbsoluteDifferenceRecommenderEvaluator.)
This test is on the GroupLens small 10k dataset. I'm using the example GroupLensEvaluatorRunner.main. I substituted the SVDRecommender for the SlopeOneRecommender in the example. Otherwise it is the GroupLens example. How many features and how many iterations are needed before the sensitivity converges? Testing all combination ranges is a little tedious on my laptop. Thanks! -- Lance Norskog [email protected]
