Imran Younus created SYSTEMML-1437:
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Summary: 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
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