Spotify provides personalized music recommendations to over 100 million active users based on their listening history and the listening history of similar users. It utilizes various recommendation approaches, including collaborative filtering using latent factor models to create lower-dimensional representations of users and songs. Spotify also uses natural language processing models on playlist data and deep learning on audio features to power recommendations. Personalizing music at Spotify's massive scale across 30 million tracks presents challenges around cold starts, repeated consumption, and measuring recommendation quality.