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From "Hai (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-15504) Could MatrixFactorizationModel support recommend for some users only ?
Date Tue, 24 May 2016 09:42:12 GMT
Hai created SPARK-15504:
---------------------------

             Summary: 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: 1.6.1, 1.6.0
         Environment: Spark 1.6.1
            Reporter: Hai
            Priority: Trivial


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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