[jira] [Issue Comment Deleted] (SPARK-13857) Feature parity for ALS ML with MLLIB

["Nick Pentreath (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Nick Pentreath updated SPARK-13857:
-----------------------------------
    Comment: was deleted

(was: My view is in practice brute-force is never going to be efficient enough for any non-trivial size problem. So I'd definitely like to incorporate the ANN stuff into the top-k recommendation here. Once SignRandomProjection is in I'll take a deeper look at item-item / user-user sim for DF-API.

We could also add a form of LSH appropriate for dot product space for user-item recs.)

> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
>                 Key: SPARK-13857
>                 URL: https://issues.apache.org/jira/browse/SPARK-13857
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Nick Pentreath
>            Assignee: 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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