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https://issues.apache.org/jira/browse/SYSTEMML-1437?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Janardhan closed SYSTEMML-1437.
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Resolution: Fixed
Fix Version/s: (was: SystemML 1.1)
SystemML 1.2
> 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 1.2
>
>
> 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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