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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-15504) Could MatrixFactorizationModel support recommend for some users only ?
Date Wed, 25 May 2016 10:52:12 GMT

     [ https://issues.apache.org/jira/browse/SPARK-15504?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Nick Pentreath resolved SPARK-15504.
------------------------------------
    Resolution: Duplicate

Please see SPARK-10802 which already exists.

For the old RDD-based API, it is unlikely that this will be supported directly. However SPARK-13857
will allow this as part of the DataFrame-based API.

> Could MatrixFactorizationModel support recommend for some users only ?
> ----------------------------------------------------------------------
>
>                 Key: SPARK-15504
>                 URL: https://issues.apache.org/jira/browse/SPARK-15504
>             Project: Spark
>          Issue Type: Wish
>          Components: MLlib
>    Affects Versions: 1.6.0, 1.6.1
>         Environment: Spark 1.6.1
>            Reporter: Hai
>            Priority: Trivial
>              Labels: features, performance
>
> I have used the ALS algorithm training a model, and I want to recommend products for
some users not all in model, so the way I can use the API of MatrixFactorizationModel is the
one -> recommendProducts(user: Int, num: Int): Array[Rating] which I should recommend the
product one by one in spark driver, or the one -> recommendProductsForUsers(num: Int):
RDD[(Int, Array[Rating])] which could run in spark cluster but it take some unused time calculate
the user that I don't want to recommend products for.  So I think if there could have an API
such as -> recommendProductsForUsers(users: RDD[Int], num: Int): RDD[(Int, Array[Rating])],
so it best  match my case. 



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