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