Methods, systems and computer-readable media for distributed probabilistic matrix factorization

A system for distributed probabilistic matrix factorization, the system comprising:

• an initializing component configured to:
• initialize a plurality of matrices by a set of values from a probability distribution function, whereby the plurality of matrices include a first mean matrix Ū, a first variance matrix Ũ and a first prior variance matrix ŨP, a second mean matrix V, a second variance matrix {tilde over (V)}, and a second prior variance matrix {tilde over (V)}P;
• a partitioning component configured to:
• partition a sparse matrix into a first set of blocks on a distributed computer cluster, whereby a dimension of each block is MB rows and NB columns;
• and
• partition the plurality of matrices into a second set of blocks and a third set of blocks on the distributed computer cluster;
• and
• an updating component configured to:
• update the plurality of matrices, iteratively until a cost function of the sparse matrix converges, whereby each iteration comprises of a plurality of MapReduce steps.

A computer program product consisting of a plurality of program instructions stored on a non-transitory computer-readable medium that, when executed by a computing device, performs a method for distributed probabilistic matrix factorization, the method comprising:

• partitioning a sparse matrix into a first set of blocks on a distributed computer cluster, whereby a dimension of each block is MB rows and NB columns.
• initializing a first mean matrix Ū, a first variance matrix Ũ, a first prior variance matrix ŨP, a second mean matrix V, a second variance matrix {tilde over (V)}, and a second prior variance matrix {tilde over (V)}P by a set of values from a probability distribution function;
• partitioning the Ū, the Ũ, and the ŨP, into a second set of blocks on the distributed computer cluster whereby a dimension of each block is the MB rows and K columns;
• partitioning the V, the {tilde over (V)}, and the {tilde over (V)}P into a third set of blocks on the distributed computer cluster whereby a dimension of each block is the NB rows and the K columns;
• and
• updating the Ū, the Ũ, the ŨP, the {tilde over (V)}, the {tilde over (V)}, and the {tilde over (V)}P iteratively until a cost function of the sparse matrix converges, whereby each iteration comprises of a plurality of MapReduce steps.