  We present a large scale hyperbolic recommender system. We discuss why
hyperbolic geometry is a more suitable underlying geometry for many
recommendation systems and cover the fundamental milestones and insights that
we have gained from its development. In doing so, we demonstrate the viability
of hyperbolic geometry for recommender systems, showing that they significantly
outperform Euclidean models on datasets with the properties of complex
networks. Key to the success of our approach are the novel choice of underlying
hyperbolic model and the use of the Einstein midpoint to define an asymmetric
recommender system in hyperbolic space. These choices allow us to scale to
millions of users and hundreds of thousands of items.
