Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
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 matrix-factorization 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.
Questions and AnswersYou need to be logged in to be able to post here.