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.

使用用户的听歌历史和个人信息，借助于度量学习及 Siamese 网络，构建出用户嵌入和音频嵌入表示，从而提出了基于内容的音乐推荐方法，并将音频嵌入作为音乐类型分类任务的特征。实验结果表明，该方法达到了最新的性能。