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Date: Wed, 25 May 2016 10:52:12 +0000 (UTC)
From: "Nick Pentreath (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12972338.1464082881000.286985.1464173532829@Atlassian.JIRA>
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Subject: [jira] [Resolved] (SPARK-15504) Could MatrixFactorizationModel
 support recommend for some users only ?
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archived-at: Wed, 25 May 2016 10:52:15 -0000


     [ 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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