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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-3066) Support recommendAll in matrix factorization model
Date Fri, 15 Aug 2014 08:27:18 GMT
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



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