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Mike Dusenberry resolved SYSTEMML-1437. --------------------------------------- Resolution: Fixed Fix Version/s: SystemML 1.1 Merged in [commit be3c1a | https://github.com/apache/systemml/commit/be3c1a6a0bf96429333ed80233d68cac97ad9284]. > 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 > Priority: Major > Labels: factorization_machines, scalability > Fix For: SystemML 1.1 > > > 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 (v7.6.3#76005)