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Music Playlist Recommendation with Long Short-Term Memory

  • Huiping Yang
  • Yan Zhao
  • Jinfu Xia
  • Bin Yao
  • Min Zhang
  • Kai ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11447)

Abstract

Music playlist recommendation is an important component in modern music streaming services, which is used for improving user experience by regularly pushing personalized music playlists based on users’ preferences. In this paper, we propose a novel music playlist recommendation problem, namely Personalized Music Playlist Recommendation (PMPR), which aims to provide a suitable playlist for a user by taking into account her long/short-term preferences and music contextual data. We propose a data-driven framework, which is comprised of two phases: user/music feature extraction and music playlist recommendation. In the first phase, we adopt a matrix factorization technique to obtain long-term features of users and songs, and utilize the Paragraph Vector (PV) approach, an advanced natural language processing technique, to capture music context features, which are the basis of the subsequent music playlist recommendation. In the second phase, we design two Attention-based Long Short-Term Memory (AB-LSTM) models, i.e., typical AB-LSTM model and Improved AB-LSTM (IAB-LSTM) model, to achieve the suitable personalized playlist recommendation. Finally, we conduct extensive experiments using a real-world dataset, verifying the practicability of our proposed methods.

Notes

Acknowledgement

This work was supported by the NSFC (61832017, 61532018, 61836007, 61872235, 61729202, U1636210), and The National Key Research and Development Program of China (2018YFC1504504).

References

  1. 1.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR (2014)Google Scholar
  2. 2.
    Bogdanov, D., Haro, M., Fuhrmann, F., Xambó, A., Gómez, E., Herrera, P.: Semantic audio content-based music recommendation and visualization based on user preference examples. IPM 49(1), 13–33 (2013)Google Scholar
  3. 3.
    Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: ACM Multimedia, pp. 211–212 (2005)Google Scholar
  4. 4.
    Cheng, Z., Shen, J.: Just-for-me: an adaptive personalization system for location-aware social music recommendation. In: ICMR, p. 185 (2014)Google Scholar
  5. 5.
    Cheng, Z., Shen, J.: On effective location-aware music recommendation. ACM TOIS 34(2), 13 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Knees, P., Schedl, M.: A survey of music similarity and recommendation from music context data. TOMCCAP 10(1), 2 (2013)CrossRefGoogle Scholar
  7. 7.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  8. 8.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)Google Scholar
  9. 9.
    Li, T., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification. In: SIGIR, pp. 282–289 (2003)Google Scholar
  10. 10.
    McFee, B., Barrington, L., Lanckriet, G.: Learning content similarity for music recommendation. TASLP 20(8), 2207–2218 (2012)Google Scholar
  11. 11.
    Van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: NIPS, pp. 2643–2651 (2013)Google Scholar
  12. 12.
    Oramas, S., Ostuni, V.C., Noia, T.D., Serra, X., Sciascio, E.D.: Sound and music recommendation with knowledge graphs. ACM TIST 8(2), 21 (2017)Google Scholar
  13. 13.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: AUAI, pp. 452–461 (2009)Google Scholar
  14. 14.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM, pp. 81–90 (2010)Google Scholar
  15. 15.
    Schedl, M., Pohle, T., Knees, P., Widmer, G.: Exploring the music similarity space on the web. ACM TOIS 29(3), 14 (2011)CrossRefGoogle Scholar
  16. 16.
    Schmidhuber, J., Wierstra, D., Gomez, F.J.: Evolino: hybrid neuroevolution/optimal linear search for sequence prediction. In: IJCAI (2005)Google Scholar
  17. 17.
    Slaney, M.: Web-scale multimedia analysis: does content matter? IEEE Multimed. 18(2), 12–15 (2011)CrossRefGoogle Scholar
  18. 18.
    Wang, D., Deng, S., Xu, G.: Sequence-based context-aware music recommendation. Inf. Retr. J. 21(2–3), 230–252 (2018)CrossRefGoogle Scholar
  19. 19.
    Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: ACM Multimedia, pp. 627–636 (2014)Google Scholar
  20. 20.
    Zangerle, E., Gassler, W., Specht, G.: Exploiting twitter’s collective knowledge for music recommendations. In: #MSM, pp. 14–17 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Huiping Yang
    • 1
  • Yan Zhao
    • 1
  • Jinfu Xia
    • 1
  • Bin Yao
    • 2
  • Min Zhang
    • 1
  • Kai Zheng
    • 3
    Email author
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Shanghai Jiao Tong UniversityShanghaiChina
  3. 3.University of Electronic Science and Technology of ChinaChengduChina

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