[jira] [Created] (SPARK-3066) Support recommendAll in matrix factorization model

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

Xiangrui Meng created SPARK-3066:
------------------------------------

             Summary: Support recommendAll in matrix factorization model
                 Key: SPARK-3066
                 URL: https://issues.apache.org/jira/browse/SPARK-3066
             Project: Spark
          Issue Type: New Feature
          Components: MLlib
            Reporter: Xiangrui Meng
            Assignee: Xiangrui Meng


ALS returns a matrix factorization model, which we can use to predict ratings for individual queries as well as small batches. In practice, users may want to compute top-k recommendations offline for all users. It is very expensive but a common problem. We can do some optimization like

1) collect one side (either user or product) and broadcast it as a matrix
2) use level-3 BLAS to compute inner products
3) use Utils.takeOrdered to find top-k



--
This message was sent by Atlassian JIRA
(v6.2#6252)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org