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https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15202240#comment-15202240
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Xiangrui Meng commented on SPARK-13857:
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+1. We need to figure out the semantics in a pipeline context and how to
connect with evaluation. Could you suggest some example code for
cross-validating ALS with a ranking metric like NDCG?
> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
> Key: SPARK-13857
> URL: https://issues.apache.org/jira/browse/SPARK-13857
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Nick Pentreath
>
> Currently {{mllib.recommendation.MatrixFactorizationModel}} has methods
> {{recommendProducts/recommendUsers}} for recommending top K to a given user /
> item, as well as {{recommendProductsForUsers/recommendUsersForProducts}} to
> recommend top K across all users/items.
> Additionally, SPARK-10802 is for adding the ability to do
> {{recommendProductsForUsers}} for a subset of users (or vice versa).
> Look at exposing or porting (as appropriate) these methods to ALS in ML.
> Investigate if efficiency can be improved at the same time (see SPARK-11968).
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