[jira] [Closed] (SYSTEMML-1437) Implement and scale Factorization Machines using SystemML

["Mike Dusenberry (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SYSTEMML-1437?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mike Dusenberry closed SYSTEMML-1437.
-------------------------------------

> 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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