Ted Dunning <ted.dunning <at> gmail.com> writes: > > WIthout more information it is impossible to comment. > > What experiments? > > On Fri, May 3, 2013 at 8:45 AM, William <icswilliam2010 <at> gmail.com> wrote: > > > I'm trying to get some recommendations with three Algorithms: > > 1.parallelALS > > 2.evaluateFactorization > > 3.recommendfactorized > > > > In my experiments, RMSE value monotonically increases with larger > > numfeatures. > > > > But Base on Netflix Prize experiment, RMSE should decreases with larger > > numfeatures. > > > > How to explain and figure out it? > > > > > > > > >
I have a dataset about user and movie(no rate).But I want to get some recommendations from this dataset. I just know the users see or not see some movie.So I set the rating matrix like: seen movies are 1, not seen movies are missing. I use parallelALS function to decompose this matrix with three parameters(numfeatures ,numIterations, lambda). And I would like to get the best combination to reduce the RMSE. I my experiment, RMSE value decreases with larger numIterations. But it increases with larger numfeatures. I use the another rating-matrix(from mahout official website) to experiment, everything is fine. So How to explain it? Can't I assign all rates are 1? U M R 1,101,1 1,102,1 2,101,1 2,103,1 2,104,1 3,101,1 3,104,1 3,105,1 4,101,1 4,103,1
