About the SVDRecommender- 10 features and 50 iterations gave evaluation scores from .70 to .81, solely by changing the random seeds.
On Tue, Aug 31, 2010 at 12:13 AM, Sean Owen <[email protected]> wrote: > 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] >> > -- Lance Norskog [email protected]
