#Nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations

  • Martin PichlEmail author
  • Eva Zangerle
  • Günther Specht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9396)


The rise of the web enabled new distribution channels like online stores and streaming platforms, offering a vast amount of different products. For helping customers finding products according to their taste on those platforms, recommender systems play an important role. Besides focusing on the computation of the recommendations itself, in literature the problem of a lack of data appropriate for research is discussed. In order to overcome this problem, we present a music recommendation system exploiting a dataset containing listening histories of users, who posted what they are listening to at the moment on the microblogging platform Twitter. As this dataset is updated daily, we propose a genetic algorithm, which allows the recommender system to adopt its input parameters to the extended dataset. In the evaluation part of this work, we benchmark the presented recommender system against two baseline approaches. We show that the performance of our proposed recommender is promising and clearly outperforms the baseline.


Music recommender systems Collaborative filtering Social media Twitter 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006)Google Scholar
  2. 2.
    Bertin-Mahieux, T., Ellis, D.P.W., Whitman, B., Lamere, P.: The million song dataset. In: Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), pp. 591–596. University of Miami (2011)Google Scholar
  3. 3.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Celma, Ò.: Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer (2010)Google Scholar
  5. 5.
    Dooms, S., De Pessemier, T., Martens, L.: Movietweetings: a movie rating dataset collected from twitter. In: 7th ACM Conference on Recommender Systems Workshop on Crowdsourcing and Human Computation for Recommender Systems (RecSys 2013) (2013)Google Scholar
  6. 6.
    Fong, S., Ho, Y., Hang, Y.: Using genetic algorithm for hybrid modes of collaborative filtering in online recommenders. In: Proceedings of the Eighth International Conference on Hybrid Intelligent Systems (HIS 2008), pp. 174–179 (2008)Google Scholar
  7. 7.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  8. 8.
    Hauger, D., Schedl, M., Kosir, A., Tkalcic, M.: The million musical tweet dataset - what we can learn from microblogs. In: Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), pp. 189–194 (2013)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  11. 11.
    Jaccard, P.: The distribution of the flora in the alpine zone. New Phytologist 11(2), 37–50 (1912)CrossRefGoogle Scholar
  12. 12.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)Google Scholar
  13. 13.
    Kim, H.-T., Kim, E., Lee, J.-H., Ahn, C.W.: A recommender system based on genetic algorithm for music data. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET 2010), vol. 6, pp. V6-414–V6-417 (2010)Google Scholar
  14. 14.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  15. 15.
    Schedl, M., Schnitzer, D.: Hybrid retrieval approaches to geospatial music recommendation. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013) (2013)Google Scholar
  16. 16.
    Schedl, M., Schnitzer, D.: Location-aware music artist recommendation. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part II. LNCS, vol. 8326, pp. 205–213. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  17. 17.
    Schedl, M., Vall, A., Farrahi, K.: User geospatial context for music recommendation in microblogs. In: Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014) (2014)Google Scholar
  18. 18.
    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: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), pp. 296–301 (2006)Google Scholar
  19. 19.
    Zangerle, E., Gassler, W., Specht, G.: Exploiting twitter’s collective knowledge for music recommendations. In: Proceedings of the 2nd Workshop on Making Sense of Microposts (#MSM 2012), pp. 14–17 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Databases and Information Systems, Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

Personalised recommendations