[jira] [Commented] (MAHOUT-1365) Weighted ALS-WR iterator for Spark

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-1365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13906724#comment-13906724 ] 

Sean Owen commented on MAHOUT-1365:
-----------------------------------

Dmitriy isn't this exactly what is already implemented in MLlib? Same paper for sure.

http://spark.incubator.apache.org/docs/latest/mllib-guide.html#collaborative-filtering-1

> Weighted ALS-WR iterator for Spark
> ----------------------------------
>
>                 Key: MAHOUT-1365
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1365
>             Project: Mahout
>          Issue Type: Task
>            Reporter: Dmitriy Lyubimov
>            Assignee: Dmitriy Lyubimov
>             Fix For: 1.0
>
>         Attachments: distributed-als-with-confidence.pdf
>
>
> Given preference P and confidence C distributed sparse matrices, compute ALS-WR solution for implicit feedback (Spark Bagel version).
> Following Hu-Koren-Volynsky method (stripping off any concrete methodology to build C matrix), with parameterized test for convergence.
> The computational scheme is following ALS-WR method (which should be slightly more efficient for sparser inputs). 
> The best performance will be achieved if non-sparse anomalies prefilitered (eliminated) (such as an anomalously active user which doesn't represent typical user anyway).
> the work is going here https://github.com/dlyubimov/mahout-commits/tree/dev-0.9.x-scala. I am porting away our (A1) implementation so there are a few issues associated with that.



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