Large-scale matrix factorization using MapReduce Conference

Sun, Z, Li, T, Rishe, N. (2010). Large-scale matrix factorization using MapReduce . Proceedings - IEEE International Conference on Data Mining, ICDM, 1242-1248. 10.1109/ICDMW.2010.155

cited authors

  • Sun, Z; Li, T; Rishe, N

abstract

  • Due to the popularity of nonnegative matrix factorization and the increasing availability of massive data sets, researchers are facing the problem of factorizing large-scale matrices of dimensions in the orders of millions. Recent research [11] has shown that it is feasible to factorize a million-by-million matrix with billions of nonzero elements on a MapReduce cluster. In this work, we present three different matrix multiplication implementations and scale up three types of nonnegative matrix factorizations on MapReduce. Experiments on both synthetic and real-world datasets show the excellent scalability of our proposed algorithms. © 2010 IEEE.

publication date

  • December 1, 2010

published in

Digital Object Identifier (DOI)

start page

  • 1242

end page

  • 1248