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https://issues.apache.org/jira/browse/MAHOUT-1464?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13941541#comment-13941541
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Dmitriy Lyubimov commented on MAHOUT-1464:
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Oh, you mean in case of sparse row vectors.
You are probably right. indeed, there's currently a SparseMatrix there in this
case. I think it should be SparseRowMatrix of course. most of the cases should
benefit from it. Problem is, like i said, mapblock doesn't really form it; nor
any other physical operator has any knowledge what formed it.
It is possible to optimize the entire operator fusion chain based on subsequent
operator preferred type, that's actually a very neat idea for in-core speed
optimization; but i have no capacity to pursue this technique at the moment. It
needs some digestion anyway (at least on my end). It requires experiments with
in-core operations. At the first glance, most non-multiplicative operators
would be ok with row-wise matrix, as well as deblockifying views.
> RowSimilarityJob on Spark
> -------------------------
>
> Key: MAHOUT-1464
> URL: https://issues.apache.org/jira/browse/MAHOUT-1464
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.9
> Environment: hadoop, spark
> Reporter: Pat Ferrel
> Labels: performance
> Fix For: 0.9
>
> Attachments: MAHOUT-1464.patch, MAHOUT-1464.patch, MAHOUT-1464.patch
>
>
> Create a version of RowSimilarityJob that runs on Spark. Ssc has a prototype
> here: https://gist.github.com/sscdotopen/8314254. This should be compatible
> with Mahout Spark DRM DSL so a DRM can be used as input.
> Ideally this would extend to cover MAHOUT-1422 which is a feature request for
> RSJ on two inputs to calculate the similarity of rows of one DRM with those
> of another. This cross-similarity has several applications including
> cross-action recommendations.
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