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MusicCNNs: A New Benchmark on Content-Based Music Recommendation

  • Guoqiang ZhongEmail author
  • Haizhen Wang
  • Wencong Jiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

In this paper, we propose a new deep convolutional neural network for content-based music recommendation, and call it MusicCNNs. To learn effective representations of the music segments, we have collected a data set including 600,000+ songs, where each song has been split into about 20 music segments. Furthermore, the music segments are converted to “images” using the Fourier transformation, so that they can be easily fed into MusicCNNs. On this collected data set, we compared MusicCNNs with other existing methods for content-based music recommendation. Experimental results show that MusicCNNs can generally deliver more accurate recommendations than the compared methods. Therefore, along with the collected data set, MusicCNNs can be considered as a new benchmark for content-based music recommendation.

Keywords

Recommendation systems Music recommendation Content-based recommendation Deep learning Music convolutional neural networks 

Notes

Acknowledgment

This work was supported by the National Key R&D Program of China under Grant 2016YFC1401004, the National Natural Science Foundation of China (NSFC) under Grant No. 41706010, the Science and Technology Program of Qingdao under Grant No. 17-3-3-20-nsh, the CERNET Innovation Project under Grant No. NGII20170416, and the Fundamental Research Funds for the Central Universities of China.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyOcean University of ChinaQingdaoChina

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