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)


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.


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



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.


  1. 1.
    Bracewell, R.N., Bracewell, R.N.: The Fourier Transform and Its Applications, vol. 31999. McGraw-Hill, New York (1986)zbMATHGoogle Scholar
  2. 2.
    Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: ACM MM, pp. 211–212 (2005)Google Scholar
  3. 3.
    Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
  4. 4.
    Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW, pp. 271–280 (2007)Google Scholar
  5. 5.
    Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative filtering by using temporal context. In: ICTAI, pp. 783–788. IEEE (2013)Google Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR Forum, vol. 51, no. 2, pp. 227–234 (2017)CrossRefGoogle Scholar
  7. 7.
    Humphrey, E.J., Bello, J.P., LeCun, Y.: Feature learning and deep architectures: new directions for music informatics. J. Intell. Inf. Syst. 41(3), 461–481 (2013)CrossRefGoogle Scholar
  8. 8.
    Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  9. 9.
    Li, D., Chen, C., Liu, W., Lu, T., Gu, N., Chu, S.M.: Mixture-rank matrix approximation for collaborative filtering. In: NIPS, pp. 477–485 (2017)Google Scholar
  10. 10.
    McFee, B., Barrington, L., Lanckriet, G.R.G.: Learning content similarity for music recommendation. CoRR abs/1105.2344 (2011)Google Scholar
  11. 11.
    Murray, M.: Building a music recommender with deep learning.
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: NIPS, pp. 2643–2651 (2013)Google Scholar
  14. 14.
    Soleymani, M., Aljanaki, A., Wiering, F., Veltkamp, R.C.: Content-based music recommendation using underlying music preference structure. In: ICME, pp. 1–6. IEEE (2015)Google Scholar
  15. 15.
    Wang, X., Rosenblum, D., Wang, Y.: Context-aware mobile music recommendation for daily activities. In: ACM MM, pp. 99–108. ACM (2012)Google Scholar
  16. 16.
    Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: ACM MM, pp. 627–636. ACM (2014)Google Scholar
  17. 17.
    Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: ISMIR, vol. 6, p. 7th (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

Personalised recommendations