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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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Labels: factorization_machines scalability (was: factorization_machines
gsoc2017 machine_learning mentor recommender_system)
> 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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