User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues
By: Eva Zangerle, Martin Pichl and Markus Schedl
References
- Adomavicius, G., & Tuzhilin, A. (2011).
Context-aware recommender systems . In Recommender Systems Handbook, pages 217–253. Springer, New York, NY, USA. DOI: 10.1007/978-0-387-85820-3_7 - Andersen, J. S. (2014). Using the Echo Nest’s automatically extracted music features for a musicological purpose. In 4th International Workshop on Cognitive Information Processing (CIP), pages 1–6.
- Ankolekar, A., & Sandholm, T. (2011). Foxtrot: A soundtrack for where you are. In IwS ’11 Proceedings of Interacting with Sound Workshop: Exploring Context-Aware, Local and Social Audio Applications, pages 26–31. New York, NY, USA.
ACM . DOI: 10.1145/2019335.2019341 - Ayaki, T., Yanagimoto, H., & Yoshioka, M. (2017). Recommendation from access logs with ensemble learning. Artificial Life and Robotics, 22(2), 163–167. DOI: 10.1007/s10015-016-0346-x
- Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Lüke, K.-H., & Schwaiger, R. (2011a). InCarMusic: Context-aware music recommendations in a car. In International Conference on Electronic Commerce and Web Technologies. DOI: 10.1007/978-3-642-23014-1_8
- Baltrunas, L., Ludwig, B., & Ricci, F. (2011b). Matrix factorization techniques for context-aware recommendation. In Proceedings of the Fifth ACM Conference on Recommender Systems, pages 301–304.
ACM . DOI: 10.1145/2043932.2043988 - Bellogin, A., Castells, P., & Cantador, I. (2011). Precision-oriented evaluation of recommender systems: An algorithmic comparison. In Proceedings of the Fifth ACM Conference on Recommender Systems, pages 333–336. New York, NY, USA.
ACM . DOI: 10.1145/2043932.2043996 - Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010). Understanding choice overload in recommender systems. In Proceedings of the Fourth ACM Conference on Recommender Systems, pages 63–70. New York, NY, USA.
ACM . DOI: 10.1145/1864708.1864724 - Braunhofer, M., Kaminskas, M., & Ricci, F. (2011). Recommending music for places of interest in a mobile travel guide. In Proceedings of the Fifth ACM Conference on Recommender Systems, pages 253–256. New York, NY, USA.
ACM . DOI: 10.1145/2043932.2043977 - Braunhofer, M., Kaminskas, M., & Ricci, F. (2013). Location-aware music recommendation. International Journal of Multimedia Information Retrieval, 2(1), 31–44. DOI: 10.1007/s13735-012-0032-2
- Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 43–52. San Francisco, CA, USA.
Morgan Kaufmann Publishers Inc . - Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., & Slaney, M. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96, 668–696. DOI: 10.1109/JPROC.2008.916370
- Celma, O. (2010). Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer Publishing, 1st edition.
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794. New York, NY, USA.
ACM . DOI: 10.1145/2939672.2939785 - Cheng, Z., & Shen, J. (2014). Just-for-me: An adaptive personalization system for location-aware social music recommendation. In Proceedings of the 2014 ACM International Conference on Multimedia Retrieval, pages 1267–1268. New York, NY, USA.
ACM . DOI: 10.1145/2600428.2611187 - Cremonesi, P., Koren, Y., & Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the Fourth ACM Conference on Recommender Systems, pages 39–46. New York, NY, USA.
ACM . DOI: 10.1145/1864708.1864721 - Cremonesi, P., Turrin, R., Lentini, E., & Matteucci, M. (2008). An evaluation methodology for collaborative recommender systems. In 2008 International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution, pages 224–231. DOI: 10.1109/AXMEDIS.2008.13
- Dahlberg, S., Holmberg, S., Rothstein, B., Khomenko, A., & Svensson, R. (2016). Quality of Government (QoG) Basic Dataset 2016. The Quality of Government Institute, University of Gothenburg.
- Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55(1), 34. DOI: 10.1037/0003-066X.55.1.34
- Dror, G., Koenigstein, N., Koren, Y., & Weimer, M. (2012). The yahoo! music dataset and KDDcup’ 11. In Proceedings of KDD Cup 2011 Competition, JMLR Proceedings, volume 18, pages 3–18.
JMLR.org . - Elkahky, A. M., Song, Y., & He, X. (2015). A multiview deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, pages 278–288.
International World Wide Web Conferences Steering Committee . DOI: 10.1145/2736277.2741667 - Ferwerda, B., & Schedl, M. (2016). Investigating the relationship between diversity in music consumption behavior and cultural dimensions: A crosscountry analysis. In Proceedings of the 24th International Conference on User Modeling, Adaptation and Personalization: Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems.
- Hauger, D., Schedl, M., Kosir, A., & Tkalcic, M. (2013). The Million Musical Tweet Dataset: What we can learn from microblogs. In Proceedings of the 14th International Society for Music Information Retrieval Conference.
- He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on WorldWideWeb, pages 173–182. Geneva, Switzerland.
International World Wide Web Conferences Steering Committee . DOI: 10.1145/3038912.3052569 - Helliwell, J. F., Layard, R., & Sachs, J. (2016). World Happiness Report. Sustainable Development Solutions Network.
- Hofstede, G. H. (1980). Culture’s Consequences: International Differences in Work-Related Values. Sage Publications, Beverly Hills, CA.
- Hofstede, G., Hofstede, G. J., & Minkov, M. (1991). Cultures and Organizations: Software of the Mind, volume 2. McGraw-Hill.
- Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 263–272. Washington, DC, USA.
IEEE Computer Society . DOI: 10.1109/ICDM.2008.22 - Hu, Y., & Ogihara, M. (2011). Nextone player: A music recommendation system based on user behavior. In Proceedings of the 12th International Society for Music Information Retrieval Conference. Miami, FL, USA.
- Kaminskas, M., Fernández-Tobías, I., Ricci, F., & Cantador, I. (2012). Knowledge-based Music Retrieval for Places of Interest. In Proceedings of the Second International ACM Workshop on Music Information Retrieval with User-centered and Multimodal Strategies, pages 19–24. New York, NY, USA.
ACM . DOI: 10.1145/2390848.2390854 - Kaminskas, M., & Ricci, F. (2012). Contextual music information retrieval and recommendation: State of the art and challenges. Computer Science Review, 6(2), 89–119. DOI: 10.1016/j.cosrev.2012.04.002
- Kaminskas, M., Ricci, F., & Schedl, M. (2013). Location-aware music recommendation using auto-tagging and hybrid matching. In Proceedings of the 7th ACM Conference on Recommender Systems, pages 17–24. New York, NY, USA.
ACM . DOI: 10.1145/2507157.2507180 - Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems, pages 79–86.
ACM . DOI: 10.1145/1864708.1864727 - Kim, J.-Y., & Belkin, N. J. (2002). Categories of music description and search terms and phrases used by non-music experts. In Proceedings of the 3rd International Conference on Music Information Retrieval, volume 2, pages 209–214.
- Knees, P., & Schedl, M. (2013). A survey of music similarity and recommendation from music context data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 10(1). DOI: 10.1145/2542205.2542206
- Knees, P., & Schedl, M. (2016). Music Similarity and Retrieval — An Introduction to Audio- and Webbased Strategies. Springer, Berlin and Heidelberg, Germany. DOI: 10.1007/978-3-662-49722-7_1
- Lee, J. H., & Downie, J. S. (2004). Survey of music information needs, uses, and seeking behaviours: Preliminary findings. In Proceedings of the 5th International Conference on Music Information Retrieval, volume 2004.
- Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. DOI: 10.1016/j.jesp.2013.03.013
- Liu, M., Hu, X., & Schedl, M. (2017). Artist preferences and cultural, socio-economic distances across countries: A big data perspective. In Proceedings of the 18th International Society for Music Information Retrieval Conference, pages 103–111.
- Liu, M., Hu, X., & Schedl, M. (2018). The relation of culture, socio-economics, and friendship to music preferences: A large-scale, cross-country study. PLOS ONE, 13(12), 1–29. DOI: 10.1371/journal.pone.0208186
- Logan, B. (2002). Content-based playlist generation: Exploratory experiments. In Proceedings of the 3rd International Conference on Music Information Retrieval, pages 295–296.
- McVicar, M., Freeman, T., & De Bie, T. (2011). Mining the correlation between lyrical and audio features and the emergence of mood. In Proceedings of the 12th International Society for Music Information Retrieval Conference, pages 783–788.
- Miotto, R., Barrington, L., & Lanckriet, G. (2010). Improving Auto-tagging by Modeling Semantic Co-occurrences. In Proceedings of the 11th International Society for Music Information Retrieval Conference.
- Pacuk, A., Sankowski, P., Wegrzycki, K., Witkowski, A., & Wygocki, P. (2016). RecSys Challenge 2016: Job recommendations based on preselection of offers and gradient boosting. In Proceedings of the Recommender Systems Challenge, pages 10:1–10:4, New York, NY, USA.
ACM . DOI: 10.1145/2987538.2987544 - Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008). One-class collaborative filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 502–511. Piscataway, NJ, USA.
IEEE . DOI: 10.1109/ICDM.2008.16 - Pichl, M., & Zangerle, E. (2018). Latent feature combination for multi-context music recommendation. In Proceedings of the Conference on Content-Based Multimedia Indexing.
IEEE . DOI: 10.1109/CBMI.2018.8516495 - Pichl, M., Zangerle, E., & Specht, G. (2016). Understanding playlist creation on music streaming platforms. In IEEE International Symposium on Multimedia, pages 475–480.
IEEE Computer Society . DOI: 10.1109/ISM.2016.0107 - Pichl, M., Zangerle, E., Specht, G., & Schedl, M. (2017). Mining culture-specific music listening behavior from social media data. In IEEE International Symposium on Multimedia, pages 208–215.
IEEE Computer Society . DOI: 10.1109/ISM.2017.35 - Schedl, M. (2013). Leveraging microblogs for spatiotemporal music information retrieval. In European Conference on Information Retrieval, pages 796–799.
Springer . DOI: 10.1007/978-3-642-36973-5_87 - Schedl, M. (2016). The LFM-1b dataset for music retrieval and recommendation. In Proceedings of the ACM International Conference on Multimedia Retrieval, pages 103–110. New York, NY, USA.
ACM . DOI: 10.1145/2911996.2912004 - Schedl, M. (2017). Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset. International Journal on Multimedia Information Retrieval, 6(1), 71–84. DOI: 10.1007/s13735-017-0118-y
- Schedl, M. (2019). Deep Learning in Music Recommendation Systems. Frontiers in Applied Mathematics and Statistics, 5, 44. DOI: 10.3389/fams.2019.00044
- Schedl, M., Lemmerich, F., Ferwerda, B., Skowron, M., & Knees, P. (2017). Indicators of country similarity in terms of music taste, cultural, and socio economic factors. In Proceedings of the 19th IEEE International Symposium on Multimedia. DOI: 10.1109/ISM.2017.55
- Schedl, M., & Schnitzer, D. (2013). Hybrid retrieval approaches to geospatial music recommendation. In Proceedings of the 35th Annual International Conference on Research and Development in Information Retrieval, pages 793–796. New York, NY, USA.
ACM . DOI: 10.1145/2484028.2484146 - Schedl, M., & Schnitzer, D. (2014). Location-aware music artist recommendation. In Proceedings of the 20th International Conference on MultiMedia Modeling, pages 205–213.
Springer . DOI: 10.1007/978-3-319-04117-9_19 - Schedl, M., Vall, A., & Farrahi, K. (2014). User geospatial context for music recommendation in microblogs. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 987–990. New York, NY, USA.
ACM . DOI: 10.1145/2600428.2609491 - Schimmack, U., Radhakrishnan, P., Oishi, S., Dzokoto, V., & Ahadi, S. (2002). Culture, personality, and subjective well-being: Integrating process models of life satisfaction. Journal of Personality and Social Psychology, 82(4), 582. DOI: 10.1037/0022-3514.82.4.582
- Tran, N. K. (2016). Classification and learning-to-rank approaches for cross-device matching at CIKM Cup 2016. arXiv preprint arXiv:1612.07117.
- Turnbull, D., Barrington, L., Torres, D., & Lanckriet, G. (2008). Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech and Language Processing, 16(2), 467–476. DOI: 10.1109/TASL.2007.913750
- Vigliensoni, G., & Fujinaga, I. (2017). The music listening histories dataset. In Proceedings of the 18th International Society for Music Information Retrieval Conference, pages 96–102.
- Wang, X., Rosenblum, D., & Wang, Y. (2012a). Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM International Conference on Multimedia, pages 99–108.
ACM . DOI: 10.1145/2393347.2393368 - Wang, X., Rosenblum, D., & Wang, Y. (2012b). Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM International Conference on Multimedia, pages 99–108. New York, NY, USA.
ACM . DOI: 10.1145/2393347.2393368 - Zangerle, E., & Pichl, M. (2018). The many faces of users: Modeling musical preference. In Proceedings of the 19th International Society for Music Information Retrieval Conference, pages 709–716.
- Zangerle, E., Pichl, M., Gassler, W., & Specht, G. (2014). #nowplaying music dataset: Extracting listening behavior from twitter. In Proceedings of the 1st International Workshop on Internet-Scale Multimedia Management, pages 21–26. DOI: 10.1145/2661714.2661719
DOI: https://doi.org/10.5334/tismir.37 | Journal eISSN: 2514-3298
Language: English
Submitted on: Jun 4, 2019
Accepted on: Nov 15, 2019
Published on: Mar 5, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2020 Eva Zangerle, Martin Pichl, Markus Schedl, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.