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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-10802) Let ALS recommend for subset of data
Date Tue, 09 May 2017 09:10:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-10802?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16002343#comment-16002343
] 

Nick Pentreath commented on SPARK-10802:
----------------------------------------

Hey folks - since the {{ALSModel}} in the ML API now supports "recommend-all" methods, this
functionality will be implemented there (see SPARK-20679). Unless there are major objections,
I advocate closing this one as "Wont Fix" once SPARK-20679 is done, since MLlib API is in
maintenance mode.

> 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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