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Department of
Computational
Perception
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PERSONALIZED MUSIC RETRIEVAL VIA MUSIC CONTENT, MUSIC CONTEXT, AND USER CONTEXT
Project Title: Personalized Music Retrieval via Music Content, Music Context, and User Context
Sponsor:
Austrian Science
Fund (Fonds zur Förderung der wissenschaftlichen Forschung,
FWF)
Project Number: P22856-N23
Duration: 48 months (Nov 2010 – Oct 2014)
Persons involved:
Abstract
One of the biggest challenges for today's users of digital music collections is how to find music that matches his or her taste depending on factors like the music content, the user's general music taste, but also his or her current emotional state, activity, situation, or surrounding. This project aims at supporting the user by modeling such aspects in terms of similarity functions, which will eventually be integrated to build a system for personalized access to music collections.
Aspects of music similarity can be broadly categorized into music content (e.g., the rhythm or timbre of a song), music context (e.g., song lyrics or terms used on an artist's Web page to describe his or her music), and user context (i.e., external human factors that may influence how a listener perceives music, for example, the listener's mood, location, used playback device, or listening situation).
In this project, we will address the third pillar of music similarity, i.e., taking into account the user context when he or she is listening to music. We are thus going to develop novel models to describe user-related aspects of music similarity, novel methods to combine the three broad dimensions of music similarity, and novel strategies to use this combined model for information retrieval (IR) tasks. These IR tasks will include, for example, a personalized music recommendation engine, user-adaptive playlist generation or an adaptive user interface to access music collections.
Facing the challenges described above, we will carry out research to accomplish the following six goals:
Publications
Journals
Music
Information Retrieval: Recent Developments and Applications Schedl, M., Gómez, E., and Urbano, J. Foundations and Trends in Information Retrieval, volume 8, number 2-3, pp. 127-261, 2014 |
A
Survey of Music Similarity and Recommendation from Music Context Data Knees, P. and Schedl, M. ACM Transactions on Multimedia Computing, Communications and Applications, 2014. |
The Neglected User in Music
Information
Retrieval Research Schedl, M., Flexer, A., and Urbano, J. Journal of Intelligent Information Systems, 2013. |
Evaluation in Music Information
Retrieval Urbano, J., Schedl, M., and Serra, X. Journal of Intelligent Information Systems, 2013. |
Harvesting Microblogs for
Contextual Music
Similarity Estimation - A Co-occurrence-based Framework Schedl, M., Hauger, D., and Urbano J. Multimedia Systems, 2013. |
Minimal
Test Collections for Low-Cost
Evaluation of Audio Music Similarity and Retrieval Systems Urbano, J. and Schedl, M. International Journal of Multimedia Information Retrieval: Special Issue on Hybrid Music Information Retrieval, 2013. |
Local
and Global Scaling Reduce Hubs in
Space Schnitzer, D., Flexer, A., Schedl, M., and Widmer, G. Journal of Machine Learning Research, volume 13, October 2012. |
#nowplaying
Madonna: A Large-Scale
Evaluation on Estimating Similarities Between Music Artists and Between
Movies from Microblogs Schedl, M. Information Retrieval, 2012, 10.1007/s10791-012-9187-y. |
User-Aware
Music Retrieval Schedl, M., Stober, S., Gómez, E., Orio, N., Liem, C.C.S. In Meinard Müller, Masataka Goto, Markus Schedl (eds.), Multimodal Music Processing Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Saarbrücken/Wadern, Germany, 2012. |
Exploring
the Music Similarity Space on the Web Schedl, M., Pohle, T., Knees, P., and Widmer, G. ACM Transactions on Information Systems, volume 29, number 3, article 14, July 2011. |
Peer-Reviewed Conference and Workshop Proceedings
Music
Similarity and Retrieval Knees, P. and Schedl, M. Springer, expected to be published in 2015. |
Music
Recommender Systems Schedl, M., Knees, P., McFee, B., Bogdanov, D., and Kaminskas, M. In Francesco Ricci, Lior Rokach, Bracha Shapira (eds.), Recommender Systems Handbook (2nd edition) Springer, expected to be published in 2015. |
Mining
User-generated Data for
Music Information Retrieval Schedl, M., Sordo, M., Koenigstein, N., and Weinsberg, U. In Marie-Francine Moens, Juanzi Li, Tat-Seng Chua (eds.), Mining of User Generated Content and Its Applications CRC Press, January 2014. |
Exploiting
Social Media for Music Information Retrieval Schedl, M. In Naeem Ramzan, Roelof van Zwol, Jong‐Seok Lee, Kai Clüver, Xian‐Sheng Hua (eds.), Social Media Retrieval Springer, December 2012. |
User-Aware
Music Retrieval Schedl, M., Stober, S., Gómez, E., Orio, N., Liem, C.C.S. In Meinard Müller, Masataka Goto, Markus Schedl (eds.), Multimodal Music Processing Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Saarbrücken/Wadern, Germany, 2012 |
Web-
and Community-based Music Information Extraction Schedl, M. In Tao Li, Mitsunori Ogihara, George Tzanetakis (eds.), Music Data Mining CRC Press/Chapman Hall, July 2011. |