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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-10802) Let ALS recommend for subset of data
Date Mon, 09 Oct 2017 08:45:01 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10802?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Nick Pentreath resolved SPARK-10802.
------------------------------------
    Resolution: Won't Fix

> Let ALS recommend for subset of data
> ------------------------------------
>
>                 Key: SPARK-10802
>                 URL: https://issues.apache.org/jira/browse/SPARK-10802
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.5.0
>            Reporter: Tomasz Bartczak
>            Priority: Minor
>
> Currently MatrixFactorizationModel allows to get recommendations for
> - single user 
> - single product 
> - all users
> - all products
> recommendation for all users/products do a cartesian join inside.
> It would be useful in some cases to get recommendations for subset of users/products
by providing an RDD with which MatrixFactorizationModel could do an intersection before doing
a cartesian join. This would make it much faster in situation where recommendations are needed
only for subset of users/products, and when the subset is still too large to make it feasible
to recommend one-by-one.



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