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Posted to issues@spark.apache.org by "Nick Pentreath (JIRA)" <ji...@apache.org> on 2017/10/09 08:45:00 UTC
[jira] [Commented] (SPARK-10802) Let ALS recommend for subset of
data
[ https://issues.apache.org/jira/browse/SPARK-10802?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16196673#comment-16196673 ]
Nick Pentreath commented on SPARK-10802:
----------------------------------------
SPARK-20679 has been completed for the new ML API. I've closed this as we won't be doing it in the RDD API as mentioned above.
> 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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