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

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

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

Janardhan updated SYSTEMML-1437:
--------------------------------
    Component/s: Algorithms

> 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
>              Labels: factorization_machines, scalability
>
> 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.
> This project aims to implement FMs as described in the first paper:
> http://www.algo.uni-konstanz.de/members/rendle/pdf/Rendle2010FM.pdf
> We'll showcase the scalability of SystemML implementation of FMs by creating an end-to-end recommendation system.
> Basic understanding of machine learning and optimization techniques is required. Will need to collaborate with the team to resolve scaling and other systems related issues.
> Rating: Medium
> Mentors:  [~iyounus], [~nakul02]



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