Recommending items to more than a billion people

This post details the development of a distributed algorithm for collaborative filtering (CF) to handle Facebook's massive datasets (100 billion ratings, over a billion users, millions of items). It addresses the limitations of standard distributed matrix factorization approaches by introducing a rotational hybrid approach that leverages worker-to-worker messaging in Apache Giraph. This new approach significantly reduces network traffic and eliminates skewed item degree problems, improving scalability for recommendation systems.