  Music streaming services heavily rely on recommender systems to improve their
users' experience, by helping them navigate through a large musical catalog and
discover new songs, albums or artists. However, recommending relevant and
personalized content to new users, with few to no interactions with the
catalog, is challenging. This is commonly referred to as the user cold start
problem. In this applied paper, we present the system recently deployed on the
music streaming service Deezer to address this problem. The solution leverages
a semi-personalized recommendation strategy, based on a deep neural network
architecture and on a clustering of users from heterogeneous sources of
information. We extensively show the practical impact of this system and its
effectiveness at predicting the future musical preferences of cold start users
on Deezer, through both offline and online large-scale experiments. Besides, we
publicly release our code as well as anonymized usage data from our
experiments. We hope that this release of industrial resources will benefit
future research on user cold start recommendation.
