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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 updated SYSTEMML-1437:
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Description:
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]
was:
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]
> 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.
> 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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