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https://issues.apache.org/jira/browse/SPARK-10802?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14907669#comment-14907669
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Tomasz Bartczak commented on SPARK-10802:
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hmm you are probably referring to method
predict(usersProducts: RDD[(Int, Int)]): RDD[Rating]
but what I am referring to are topK recommendations for subset of users.
using
recommendProducts(user: Int, num: Int): Array[Rating] is quite slow when done
in loop for many users
recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] is an overhead
when I need that for a subset of users
I imagine a method:
recommendProductsForUsers(users:RDD[Int], num: Int): RDD[(Int, Array[Rating])]
that would first retain user features for given users and after that do a
cartesian join with product features.
> Let ALS recommend for subset of data
> ------------------------------------
>
> Key: SPARK-10802
> URL: https://issues.apache.org/jira/browse/SPARK-10802
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.5.0
> Reporter: Tomasz Bartczak
>
> Currently MatrixFactorizationModel allows to get recommendations for
> - single user
> - single product
> - all users
> - all products
> recommendation for all users/products do a cartesian join inside.
> It would be useful in some cases to get recommendations for subset of
> users/products by providing an RDD with which MatrixFactorizationModel could
> do an intersection before doing a cartesian join. This would make it much
> faster in situation where recommendations are needed only for subset of
> users/products, and when the subset is still too large to make it feasible to
> recommend one-by-one.
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