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