Music Recommendation System Design Based on Gaussian Mixture Model

DOI

10.2991/icmmcce-15.2015.182How to use a DOI?

Keywords

Gaussian Mixture Model; Music Classification; Music Recommendation

Abstract

The paper establishes a double-layer classifier based on Gaussian Mixture Model and the Thayer model to divide the music style into several categories. On the basis of effective verification of experiments, the music listening experience is added into the model to analyze and normalize two-dimensional data points and the tastes of music users or playing times also can be added to obtain new three-dimensional data points. Then the Gaussian Mixture Model is employed again for classifying the new three-dimensional data points. In this way, not only can the taste changing process of users for different music be analyzed, but also the similarity among different users can be calculated. Therefore, music can be recommended properly to music users.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yang Lu
AU  - Xuemei Bai
AU  - Feng Wang
PY  - 2015/12
DA  - 2015/12
TI  - Music Recommendation System Design Based on Gaussian Mixture Model
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 1242
EP  - 1247
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.182
DO  - 10.2991/icmmcce-15.2015.182
ID  - Lu2015/12
ER  -