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We present Factorbird, a prototype of a parameter server approach forfactorizing large matrices with Stochastic Gradient Descent-based algorithms.We designed Factorbird to meet the following desiderata: (a) scalability totall and wide matrices with dozens of billions of non-zeros, (b) extensibilityto different kinds of models and loss functions as long as they can beoptimized using Stochastic Gradient Descent (SGD), and (c) adaptability to bothbatch and streaming scenarios. Factorbird uses a parameter server in order toscale to models that exceed the memory of an individual machine, and employslock-free Hogwild!-style learning with a special partitioning scheme todrastically reduce conflicting updates. We also discuss other aspects of thedesign of our system such as how to efficiently grid search for hyperparametersat scale. We present experiments of Factorbird on a matrix built from a subsetof Twitter's interaction graph, consisting of more than 38 billion non-zerosand about 200 million rows and columns, which is to the best of our knowledgethe largest matrix on which factorization results have been reported in theliterature.

Factorbird - a Parameter Server Approach to Distributed Matrix Factorization