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https://issues.apache.org/jira/browse/SYSTEMML-1437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16347459#comment-16347459
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Mike Dusenberry edited comment on SYSTEMML-1437 at 1/31/18 7:33 PM:
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Merged in [commit
be3c1a|https://github.com/apache/systemml/commit/be3c1a6a0bf96429333ed80233d68cac97ad9284].
was (Author: [email protected]):
Merged in [commit be3c1a |
https://github.com/apache/systemml/commit/be3c1a6a0bf96429333ed80233d68cac97ad9284].
> Implement and scale Factorization Machines using SystemML
> ---------------------------------------------------------
>
> Key: SYSTEMML-1437
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1437
> Project: SystemML
> Issue Type: Task
> Components: Algorithms
> Reporter: Imran Younus
> Assignee: Janardhan
> Priority: Major
> Labels: factorization_machines, scalability
> Fix For: SystemML 1.1
>
>
> Factorization Machines have gained popularity in recent years due to their
> effectiveness in recommendation systems. FMs are general predictors which
> allow to *capture interactions between all features* in a features matrix.
> The feature matrices pertinent to the recommendation systems are highly
> sparse. SystemML's highly efficient distributed sparse matrix operations can
> be leveraged to implement FMs in a scalable fashion. Given the closed model
> equation of FMs, the model parameters can be learned using gradient descent
> methods.
> Implementation of factorization machines, as described in the paper, as a
> core +fm.dml+ module to support
> * Regression
> * Binary classification
> * Ranking
> We'll showcase the scalability of SystemML, with an end-to-end recommender
> system. Possibly, we could integrate some other algorithms to build a
> state-of-the-art recommender system.
> paper: http://www.algo.uni-konstanz.de/members/rendle/pdf/Rendle2010FM.pdf
> Mentors: [~iyounus], [~nakul02], [~dusenberrymw]
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