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From "Nakul Jindal (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SYSTEMML-1437) Implement and scale Factorization Machines using SystemML
Date Mon, 22 May 2017 17:57:04 GMT

    [ https://issues.apache.org/jira/browse/SYSTEMML-1437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16019912#comment-16019912
] 

Nakul Jindal commented on SYSTEMML-1437:
----------------------------------------

Hi [~anantbietec], unfortunately this project wasn't accepted by GSoC 2017 (as I pointed out
to Janardhan).
We encourage you, however, to work on this project, if you really are interested and can spare
the time. Since this isn't a GSoC project, I don't think there is any sort of monetary compensation
involved.
If the part about contributing to open source interests you, you can work on this project
as a contributor. You can use either this JIRA or the mailing list to discuss your ideas and
then open a Pull Request to facilitate further discussion. 

> Implement and scale Factorization Machines using SystemML
> ---------------------------------------------------------
>
>                 Key: SYSTEMML-1437
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1437
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Imran Younus
>              Labels: factorization_machines, gsoc2017, machine_learning, mentor, recommender_system
>
> 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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