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Posted to issues@systemml.apache.org by "Imran Younus (JIRA)" <ji...@apache.org> on 2017/03/25 00:20:41 UTC
[jira] [Updated] (SYSTEMML-1437) Implement and scale Factorization
Machines using SystemML
[ https://issues.apache.org/jira/browse/SYSTEMML-1437?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Imran Younus updated SYSTEMML-1437:
-----------------------------------
Description:
Factorization Machines have gained popularity in recent years due to their effectiveness in recommendation system. FMs are general predictors which allow to capture interaction 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 parameter 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
> Environment: Factorization Machines have gained popularity in recent years due to their effectiveness in recommendation system. FMs are general predictors which allow to capture interaction 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 parameter 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]
> 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 system. FMs are general predictors which allow to capture interaction 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 parameter 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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