Topic: recommendation systems

Floris Geerts analyses the expressive power of graph-learning methods. Predictions on graph-structured data must respect isomorphism, motivating invariant operations such as neighbourhood aggregation. The lecture connects these compositional operations with database theory to explain what different graph-learning architectures can represent, with applications including molecules, social networks and recommendation systems.

Finding similar item pairs is a core task in recommendation systems. It can be expressed as finding large inner products in a collection of vectors, or large entries in a matrix product. This talk describes how randomized sampling ideas became a distributed algorithm used in Twitter’s production recommendation system. Existing approaches struggled with industrial-scale inputs containing hundreds of billions of nonzero entries. The algorithm combines low-dimensional projections, called hashes, with path-sampling techniques, called wedges. The presentation explains the mathematical ideas behind