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  1. SystemDS
  2. SYSTEMDS-1437

Implement and scale Factorization Machines using SystemML

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      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: Imran Younus, Nakul Jindal, Mike Dusenberry

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              • Assignee:
                return_01 Janardhan
                Reporter:
                iyounus Imran Younus
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