Is there a fast way to do fast top N product recommendations for all users

[Crystal Xing]

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 
<http://spark.apache.org/docs/1.2.0/api/java/org/apache/spark/mllib/recommendation/Rating.html>[]
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
<http://spark.apache.org/docs/1.2.0/api/java/org/apache/spark/api/java/JavaRDD.html><Rating
<http://spark.apache.org/docs/1.2.0/api/java/org/apache/spark/mllib/recommendation/Rating.html>>
predict(JavaPairRDD
<http://spark.apache.org/docs/1.2.0/api/java/org/apache/spark/api/java/JavaPairRDD.html><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.