Hybrid Personalized Music Recommendation Method Based on Feature Increment

  • Guimei Liu
  • Wenjun JiangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems make attempts to associate music with the user’s preferences primarily based on emotions in music audio. However, this kind of recommendation mechanism ignores the emotions in lyrics and comment texts and does not consider the followers of the user, which makes the predictions unreliable. To cope with this problem, in this paper, we study the user’s listening behavior to discover his or her listening intention. We make three progresses. (1) We analyze the correlation between user preferences and the emotional categories of songs. (2) We analyze the similarity of the emotional categories of songs that users and their followees listen to. (3) We build a classification model based on KMeans and adjust different features(the correlation between the emotional category of song and user preferences, and the similarity between users and their followees) to predict whether the user will listen to the song. The experiment results verify that it is effective to consider the similarity and the correlation, and the similarity takes more effects.


Social relationship Emotion snalysis Music recommendation 



This research was supported by NSFC grant 61632009 and Outstanding Young Talents Training Program in Hunan University 531118040173.


  1. 1.
    An, Y., Sun, S., Wang, S.: Naive Bayes classifiers for music emotion classification based on lyrics. In: IEEE ACIS, pp. 635–638 (2017)Google Scholar
  2. 2.
    Bu, J., et al.: Music recommendation by unified hypergraph: combining social media information and music content. In: ACM Multimedia, pp. 391–400 (2010)Google Scholar
  3. 3.
    Chen, X., Tang, T.Y.: Combining content and sentiment analysis on lyrics for a lightweight emotion-aware Chinese song recommendation system. In: ICML pp. 85–89 (2018)Google Scholar
  4. 4.
    Choi, J., Song, J., Kim, Y.: An analysis of music lyrics by measuring the distance of emotion and sentiment. In: IEEE SNPD, pp. 176–181 (2018)Google Scholar
  5. 5.
    De Assuncao, W.G., de Almeida Neris, V.P.: An algorithm for music recommendation based on the user’s musical preferences and desired emotions. In: MUM, pp. 205–213 (2018)Google Scholar
  6. 6.
    Deng, S., Wang, D., Li, X., Xu, G.: Exploring user emotion in microblogs for music recommendation. Expert Syst. Appl. 42(23), 9284–9293 (2015)CrossRefGoogle Scholar
  7. 7.
    Feng, Y., Zhuang, Y., Pan, Y.: Music information retrieval by detecting mood via computational media aesthetics. In: Web Intelligence, pp. 235–241 (2003)Google Scholar
  8. 8.
    Feng, Y., Zhuang, Y., Pan, Y.: Popular music retrieval by detecting mood. In: ACM SIGIR, pp. 375–376 (2003)Google Scholar
  9. 9.
    Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980)CrossRefGoogle Scholar
  10. 10.
    Jannach, D., Kamehkhosh, I., Lerche, L.: Leveraging multi-dimensional user models for personalized next-track music recommendation. In: ACM SAC, pp. 1635–1642 (2017)Google Scholar
  11. 11.
    Jiang, W., Wang, G., Bhuiyan, Z.A., Wu, J.: Understanding graph-based trust evaluation in online social networks: methodologies and challenges. ACM Comput. Surv. 49(1), 10 (2016)CrossRefGoogle Scholar
  12. 12.
    Jiang, W., Wu, J.: Active opinion-formation in online social networks. In: IEEE INFOCOM, pp. 1–9 (2017)Google Scholar
  13. 13.
    Jiang, W., Wu, J., Wang, G.: On selecting recommenders for trust evaluation in online social networks. ACM Trans. Internet Technol. 15(4), 14 (2015)CrossRefGoogle Scholar
  14. 14.
    Jiang, W., Wu, J., Wang, G., Zheng, H.: Forming opinions via trusted friends: time-evolving rating prediction using fluid dynamics. IEEE Trans. Comput. 65(4), 1211–1224 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kelen, D., Berecz, D., Beres, F., Benczur, A.A.: Efficient K-NN for playlist continuation. In: ACM RecSys (2018)Google Scholar
  16. 16.
    Li, M., Jiang, W., Li, K.: When and what music will you listen to? Fine-grained time-aware music recommendation. In: IEEE ISPA, pp. 1091–1098 (2017)Google Scholar
  17. 17.
    Mahedero, J.P.G., Martinez, A., Cano, P., Koppenberger, M., Gouyon, F.: Natural language processing of lyrics. In: ACM Multimedia, pp. 475–478 (2005)Google Scholar
  18. 18.
    Mao, K., Chen, G., Hu, Y., Zhang, L.: Music recommendation using graph based quality model. Sig. Process. 120, 806–813 (2016)CrossRefGoogle Scholar
  19. 19.
    Li, M., Jiang, W., Li, K.: Recommendation systems in real applications: algorithm and parallel architecture. In: Wang, G., Ray, I., Alcaraz Calero, J.M., Thampi, S.M. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 45–58. Springer, Cham (2016). Scholar
  20. 20.
    Nakamura, K., Fujisawa, T., Kyoudou, T.: Music recommendation system using lyric network. In: IEEE GCCE, pp. 1–2 (2017)Google Scholar
  21. 21.
    Pacula, M.: A matrix factorization algorithm for music recommendation using implicit user feedback (2009)Google Scholar
  22. 22.
    Re, T.: The Biopsychology of Mood and Arousal. Oxford University Press, Oxford (1989)Google Scholar
  23. 23.
    Sanchezmoreno, D., Gonzalez, A.B.G., Vicente, M.D.M., Batista, V.F.L., Garcia, M.N.M.: A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Syst. Appl. 66, 234–244 (2016)CrossRefGoogle Scholar
  24. 24.
    Wang, X., Chen, X., Yang, D., Wu, Y.: Music emotion classification of Chinese songs based on lyrics using tf*idf and rhyme. In: ISMIR, pp. 765–770 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina

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