  Recommendation systems that automatically generate personalized music
playlists for users have attracted tremendous attention in recent years.
Nowadays, most music recommendation systems rely on item-based or user-based
collaborative filtering or content-based approaches. In this paper, we propose
a novel mixture hidden Markov model (HMM) for music play sequence prediction.
We compare the mixture model with state-of-the-art methods and evaluate the
predictions quantitatively and qualitatively on a large-scale real-world
dataset in a Kaggle competition. Results show that our model significantly
outperforms traditional methods as well as other competitors. We conclude by
envisioning a next-generation music recommendation system that integrates our
model with recent advances in deep learning, computer vision, and speech
techniques, and has promising potential in both academia and industry.
