systemml-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Janardhan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-1437) Implement and scale Factorization Machines using SystemML
Date Tue, 18 Jul 2017 05:58:00 GMT

     [ 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]



--
This message was sent by Atlassian JIRA
(v6.4.14#64029)

Mime
View raw message