Thanks, Sean! Glad to know it will be in the future release. On Thu, Feb 12, 2015 at 2:45 PM, Sean Owen <so...@cloudera.com> wrote:
> 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. >
