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Large-scale Matrix Factorization with Distributed Stochastic Gradient Descent (2011)

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by Rainer Gemulla , Peter J. Haas , Yannis Sismanis , Christina Teflioudi , Faraz Makari
Venue:In KDD
Citations:68 - 7 self
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BibTeX

@INPROCEEDINGS{Gemulla11large-scalematrix,
    author = {Rainer Gemulla and Peter J. Haas and Yannis Sismanis and Christina Teflioudi and Faraz Makari},
    title = {Large-scale Matrix Factorization with Distributed Stochastic Gradient Descent},
    booktitle = {In KDD},
    year = {2011},
    pages = {69--77}
}

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Abstract

We provide a novel algorithm to approximately factor large matrices with millions of rows, millions of columns, and billions of nonzero elements. Our approach rests on stochastic gradient descent (SGD), an iterative stochastic optimization algorithm. Based on a novel “stratified ” variant of SGD, we obtain a new matrixfactorization algorithm, called DSGD, that can be fully distributed and run on web-scale datasets using, e.g., MapReduce. DSGD can handle a wide variety of matrix factorizations and has good scalability properties. 1

Keyphrases

distributed stochastic gradient descent    large-scale matrix factorization    large matrix    novel stratified variant    nonzero element    stochastic gradient descent    matrix factorization    wide variety    good scalability property    novel algorithm    web-scale datasets    new matrixfactorization algorithm    iterative stochastic optimization algorithm   

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