I don't have any good rules of thumb for you -- maybe the author can chime in. It should be a fairly standard implementation and I would not expect unusual behavior in this regard, but can't say I know either way.
But are you asking a question about the recommender or the evaluator? On Tue, Aug 31, 2010 at 7:35 AM, Lance Norskog <[email protected]> wrote: > Hi- > > How many features and how many iterations should make the > SVDRecommender converge? > > I used the GroupLens example and 10k dataset with the SVDRecommender > instead of the SlopeOneRecommender. The > AverageAbsoluteDifferenceRecommenderEvaluator is very sensitive to the > random seed. It's on my laptop, so experimenting with # of features > and # of iterations got impossible really fast. > > -- > Lance Norskog > [email protected] >
