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From "Janardhan (JIRA)" <>
Subject [jira] [Issue Comment Deleted] (SYSTEMML-1437) Implement and scale Factorization Machines using SystemML
Date Mon, 22 May 2017 04:30:04 GMT


Janardhan updated SYSTEMML-1437:
    Comment: was deleted

(was: Sir, I want to take up this issue as per my proposal schedule. The proposal link is
 . If anybody is working on this issue, please mention here. 

I will be starting from 20th may.)

> Implement and scale Factorization Machines using SystemML
> ---------------------------------------------------------
>                 Key: SYSTEMML-1437
>                 URL:
>             Project: SystemML
>          Issue Type: Task
>            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 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:
> 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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