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

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

     [ https://issues.apache.org/jira/browse/SPARK-3066?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng updated SPARK-3066:
---------------------------------
    Target Version/s:   (was: 1.2.0)

> 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
>
> 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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