Janardhan closed SYSTEMML-1437.
       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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