Not now, but see https://issues.apache.org/jira/browse/SPARK-3066
As an aside, it's quite expensive to make recommendations for all users. IMHO this is not something to do, if you can avoid it architecturally. For example, consider precomputing recommendations only for users whose probability of needing recommendations soon is not very small. Usually, only a small number of users are active. On Thu, Feb 12, 2015 at 10:26 PM, Crystal Xing <crystalxin...@gmail.com> wrote: > Hi, > > > I wonder if there is a way to do fast top N product recommendations for all > users in training using mllib's ALS algorithm. > > I am currently calling > > public Rating[] recommendProducts(int user, > int num) > > method in MatrixFactorizatoinModel for users one by one > and it is quite slow since it does not operate on RDD input? > > I also tried to generate all possible > user-product pairs and use > public JavaRDD<Rating> predict(JavaPairRDD<Integer,Integer> usersProducts) > > to fill out the matrix. Since I have a large number of user and products, > > the job stucks and transforming all pairs. > > > I wonder if there is a better way to do this. > > Thanks, > > Crystal. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
