Distributed matrix factorization with mapreduce using a series of broadcast-joins
Sebastian Schelter, Christoph Boden, Martin Schenck, Alexander K. Alexandrov, Volker Markl · 2013
The efficient, distributed factorization of large matrices on clusters of commodity machines is crucial to applying latent factor models in industrial-scale recommender systems. We propose an efficient, data-parallel low-rank matrix factorization with Alternating Least Squares which uses a series of broadcast-joins that can be efficiently executed with MapReduce.