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! >
