Thanks Sean. I realized that I was supplying train() with a very low rank so I will retry with something higher and then play with lambda as-needed.
On Tue, Jun 10, 2014 at 4:58 PM, Sean Owen <so...@cloudera.com> wrote: > For trainImplicit(), the output is an approximation of a matrix of 0s > and 1s, so the values are generally (not always) in [0,1] > > But for train(), you should be predicting the original input matrix > as-is, as I understand. You should get output in about the same range > as the input but again not necessarily 1-5. If it's really different, > you could be underfitting. Try less lambda, more features? > > On Tue, Jun 10, 2014 at 4:59 PM, Sandeep Parikh <sand...@clusterbeep.org> > wrote: > > Question on the input and output for ALS.train() and > > MatrixFactorizationModel.predict(). > > > > My input is list of Ratings(user_id, product_id, rating) and my ratings > are > > one a scale of 1-5 (inclusive). When I compute predictions over the > superset > > of all (user_id, product_id) pairs, the ratings produced are on a > different > > scale. > > > > The question is this: do I need to normalize the data coming out of > > predict() to my own scale or does the input need to be different? > > > > Thanks! > > >
