  Personalized recommendation on new track releases has always been a
challenging problem in the music industry. To combat this problem, we first
explore user listening history and demographics to construct a user embedding
representing the user's music preference. With the user embedding and audio
data from user's liked and disliked tracks, an audio embedding can be obtained
for each track using metric learning with Siamese networks. For a new track, we
can decide the best group of users to recommend by computing the similarity
between the track's audio embedding and different user embeddings,
respectively. The proposed system yields state-of-the-art performance on
content-based music recommendation tested with millions of users and tracks.
Also, we extract audio embeddings as features for music genre classification
tasks. The results show the generalization ability of our audio embeddings.
