The data science team at iHeartRadio has been developing collaborative filtering models to drive a wide variety of features, including recommendations and radio personalization. Collaborative filtering is a popular approach in recommendation systems that makes predictive suggestions to users based on the behavior of other users in a service. For example, it’s often used to recommend new artists to users based on what they and similar users are listening to. In this blog post we discuss our initial experiences applying these models to increase user engagement through targeted marketing campaigns.

One of the most successful approaches to collaborative filtering in recent years has been matrix factorization, which decomposes all the activity on a service into compact representations of its users and what they interact with, e.g. artists. At iHeartRadio we use matrix factorization models to represent our users and artists with ~100 numbers each (called their latent vectors) instead of processing the trillions of possible interactions between them. This simplifies our user activity by many orders of magnitude, and allows us to quickly match artists to users by applying a simple scoring function to the user’s and artist’s latent vectors.