[jira] [Commented] (SPARK-13857) Feature parity for ALS ML with MLLIB

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15227103#comment-15227103 ] 

Joseph K. Bradley commented on SPARK-13857:
-------------------------------------------

Linking [SPARK-14412] which is the only other missing item I know of for ALS

> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
>                 Key: SPARK-13857
>                 URL: https://issues.apache.org/jira/browse/SPARK-13857
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Nick Pentreath
>            Assignee: Nick Pentreath
>
> Currently {{mllib.recommendation.MatrixFactorizationModel}} has methods {{recommendProducts/recommendUsers}} for recommending top K to a given user / item, as well as {{recommendProductsForUsers/recommendUsersForProducts}} to recommend top K across all users/items.
> Additionally, SPARK-10802 is for adding the ability to do {{recommendProductsForUsers}} for a subset of users (or vice versa).
> Look at exposing or porting (as appropriate) these methods to ALS in ML. 
> Investigate if efficiency can be improved at the same time (see SPARK-11968).



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