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https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15777650#comment-15777650
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Debasish Das edited comment on SPARK-13857 at 12/26/16 5:57 AM:
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item->item and user->user was done in an old PR I had...if there is interest I
can resend it...nice to see how it compares with approximate nearest neighbor
work from uber:
https://github.com/apache/spark/pull/6213
was (Author: debasish83):
item->item and user->user was done in an old PR I had...if there is interested
I can resend it...nice to see how it compares with approximate nearest neighbor
work from uber:
https://github.com/apache/spark/pull/6213
> 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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