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https://issues.apache.org/jira/browse/MAHOUT-1272?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13702175#comment-13702175
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Peng Cheng edited comment on MAHOUT-1272 at 7/8/13 6:06 PM:
------------------------------------------------------------

Hey Sebastian, Hudson, Thank you so much for on pushing things that hard. I own 
you this.
I'll test more grouplens data. Since Sebastian has taken over the code, new 
test cases will only be posted as code snippets.
                
      was (Author: peng):
    Hey Sebastian, Hudson, Thank you so much for on pushing things that hard. I 
own you this.
testing on netflix dataset has encountered some trouble, namely, I don't know 
where to download it :-<. Great appreciation for anyone who can share his 
judging.txt. In the mean time I'll try more grouplens data.
Since Sebastian has taken over the code, new test cases will only be posted as 
code snippets.
                  
> Parallel SGD matrix factorizer for SVDrecommender
> -------------------------------------------------
>
>                 Key: MAHOUT-1272
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1272
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Collaborative Filtering
>            Reporter: Peng Cheng
>            Assignee: Sean Owen
>              Labels: features, patch, test
>             Fix For: 0.8
>
>         Attachments: GroupLensSVDRecomenderEvaluatorRunner.java, 
> mahout.patch, ParallelSGDFactorizer.java, ParallelSGDFactorizer.java, 
> ParallelSGDFactorizerTest.java, ParallelSGDFactorizerTest.java
>
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> a parallel factorizer based on MAHOUT-1089 may achieve better performance on 
> multicore processor.
> existing code is single-thread and perhaps may still be outperformed by the 
> default ALS-WR.
> In addition, its hardcoded online-to-batch-conversion prevents it to be used 
> by an online recommender. An online SGD implementation may help build 
> high-performance online recommender as a replacement of the outdated 
> slope-one.
> The new factorizer can implement either DSGD 
> (http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf) or 
> hogwild! (www.cs.wisc.edu/~brecht/papers/hogwildTR.pdf).
> Related discussion has been carried on for a while but remain inconclusive:
> http://web.archiveorange.com/archive/v/z6zxQUSahofuPKEzZkzl

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