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arXiv · 1809.00842

Music Sequence Prediction with Mixture Hidden Markov Models

Tao Li·Minsoo Choi·Kaiming Fu·Lei Lin
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Abstract

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.

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Tao Li, Minsoo Choi, Kaiming Fu, Lei Lin. 2018-09-04. Music Sequence Prediction with Mixture Hidden Markov Models. https://doi.org/10.1109/bigdata47090.2019.9005695

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