Abstract
Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253. Springer (2011)
Farrahi, K., Schedl, M., Vall, A., Hauger, D., Tkalčič, M.: Impact of listening behavior on music recommendation. In: Proc. ISMIR (October 2014)
Gillhofer, M., Schedl, M.: Iron maiden while jogging, debussy for dinner? In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015, Part II. LNCS, vol. 8936, pp. 380–391. Springer, Heidelberg (2015)
Hauger, D., Schedl, M., Košir, A., Tkalčič, M.: The million musical tweets dataset: what can we learn from microblogs. In: Proc. ISMIR (November 2013)
Lamere, P.: Social Tagging and Music Information Retrieval. New Music Research: Special Issue: From Genres to Tags - Music Information Retrieval in the Age of Social Tagging 37(2), 101–114 (2008)
Schedl, M.: Ameliorating music recommendation: integrating music content, music context, and user context for improved music retrieval and recommendation. In: Proc. MoMM (December 2013)
Schedl, M.: Leveraging microblogs for spatiotemporal music information retrieval. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 796–799. Springer, Heidelberg (2013)
Schedl, M., Breitschopf, G., Ionescu, B.: Mobile music genius: reggae at the beach, metal on a friday night? In: Proc. ACM ICMR (April 2014)
Schedl, M., Hauger, D.: Tailoring music recommendations to users by considering diversity, mainstreaminess, and novelty. In: Proc. ACM SIGIR (August 2015)
Schedl, M., Hauger, D., Farrahi, K., Tkalčič, M.: On the influence of user characteristics on music recommendation. In: Proc. ECIR (March-April 2015)
Schedl, M., Schnitzer, D.: Hybrid retrieval approaches to geospatial music recommendation. In: Proc. ACM SIGIR (July-August) (2013)
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)
Schedl, M., Tkalčič, M.: Genre-based analysis of social media data on music listening behavior. In: Proc. ACM Multimedia Workshop ISMM (November 2014)
Schedl, M., Vall, A., Farrahi, K.: User geospatial context for music recommendation in microblogs. In: Proc. ACM SIGIR (July 2014)
Schnitzer, D., Flexer, A., Schedl, M., Widmer, G.: Local and Global Scaling Reduce Hubs in Space. Journal of Machine Learning Research 13, 2871–2902 (2012)
Shi, Y., Larson, M., Hanjalic, A.: Collaborative Filtering Beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges. ACM Comput. Surv. 47(1), 3:1–3:45 (2014)
Zhang, Y.C., Seaghdha, D.O., Quercia, D., Jambor, T.: Auralist: Introducing Serendipity into Music Recommendation. In: Proc. WSDM, February 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Schedl, M. (2015). Listener-Aware Music Recommendation from Sensor and Social Media Data. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-23461-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23460-1
Online ISBN: 978-3-319-23461-8
eBook Packages: Computer ScienceComputer Science (R0)