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https://issues.apache.org/jira/browse/SPARK-7008?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14502646#comment-14502646
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Sean Owen commented on SPARK-7008:
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[~podongfeng] see
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-ContributingNewAlgorithmstoMLLib
I think you would need to address those questions first. The general default
is to not start by putting the algorithm into Spark, but by hosting it yourself
first and adding it to spark-packages.org. If it proves popular it might be
considered for Spark, but not immediately.
> An Implement of Factorization Machine (LibFM)
> ---------------------------------------------
>
> Key: SPARK-7008
> URL: https://issues.apache.org/jira/browse/SPARK-7008
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.3.0, 1.3.1, 1.3.2
> Reporter: zhengruifeng
> Labels: features, patch
>
> An implement of Factorization Machines based on Scala and Spark MLlib.
> Factorization Machine is a kind of machine learning algorithm for
> multi-linear regression, and is widely used for recommendation.
> Factorization Machines works well in recent years' recommendation
> competitions.
> Ref:
> http://libfm.org/
> http://doi.acm.org/10.1145/2168752.2168771
> http://www.inf.uni-konstanz.de/~rendle/pdf/Rendle2010FM.pdf
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