Music Sequence Prediction with Mixture Hidden Markov Models
This addresses the problem of personalized music recommendation for users, but it appears incremental as it builds on existing HMM approaches.
The authors tackled music playlist prediction by proposing a novel mixture hidden Markov model, which significantly outperformed state-of-the-art methods on a large-scale real-world dataset in a Kaggle competition.
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.