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From "Nathaniel Wendt (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-22619) Implement the CG method for ALS
Date Mon, 27 Nov 2017 19:05:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-22619?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16267270#comment-16267270
] 

Nathaniel Wendt commented on SPARK-22619:
-----------------------------------------

I've implemented the algorithm and tested it's correctness but I am having trouble actually
seeing a performance speedup, likely due to incorrect handling of RDD persistence/checkpointing.
 I wasn't sure the best way to reach out to see if there were dev cycles available to collaborate
on completing this solution but I figure it has the potential to have a big impact within
Spark and MLLib.  If there is interest, I can open a pull request with the functionally correct
code I have as of now.

> Implement the CG method for ALS
> -------------------------------
>
>                 Key: SPARK-22619
>                 URL: https://issues.apache.org/jira/browse/SPARK-22619
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Nathaniel Wendt
>
> The conjugate gradient method has been shown to be very efficient at solving the least
squares error problem in matrix factorization: http://www.benfrederickson.com/fast-implicit-matrix-factorization/.
 Implementing this in Spark could mean a significant speedup in ALS solving as the order of
growth is smaller than the default solver (Cholesky).  This has the potential to improve the
training phase of collaborative filtering significantly.



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