Adaptive User Modeling for Content-Based Music Retrieval
An approach to adapt a content-based music retrieval system (CBMR system) to the user is presented and evaluated. Accepted and rejected songs are gathered to extract the user’s preferences. To compare acoustic characteristics of music files, profiles are introduced. These are based on result lists. Each result list is created by a classifier and sorted accordingly to the similarity of the given seed song. To detect important characteristics, the accepted and rejected songs are clustered with k-means. A score for each candidate song is specified by the distance to the mean values of the obtained clusters. The songs are proposed by creating a playlist, which is sorted by the score. Songs accepted by the listener are used to query the CBMR system for new songs and thus extract additional profiles. It is shown that incorporating relevance feedback can significantly improve the quality of music recommendation. The L2 distance is suitable to determine similarities between profiles of regarded songs. Introducing more than one query song during the recommendation process can further improve the quality.
Unable to display preview. Download preview PDF.
- 2.Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behaviour. In: International Conference on Music Information Retrieval, vol. 6, pp. 634–637 (2005)Google Scholar
- 3.Logan, B.: Music Recommendation From Song Sets. In: International Conference on Music Information Retrieval, vol. 5, pp. 425–428 (2004)Google Scholar
- 4.Lampropoulos, A., Sotiropoulos, D., Tsihrintzis, G.: Individualization of Music Similarity Preception via Feature Subset Selection. In: IEEE International Conference on Systems, Man & Cybernetics, vol. 1, pp. 552–556 (2004)Google Scholar
- 5.Peeters, G.: A Large Set of Audio Features for Sound Description (Similarity and Classification) in the CUIDADO Project. Technical Report, IRCAM, Paris, France (2004)Google Scholar
- 6.Dittmar, C., Bastuck, C., Gruhne, M.: Novel Mid-Level Audio Features for Music Similarity. In: International Conference on Music Communication Science, pp. 38–41 (2007)Google Scholar
- 7.Bastuck, C.: Weiterentwicklung eines Verfahrens zur automatischen Bestimmung musikalischer Ähnlichkeit. Master’s Thesis, University Siegen (2006)Google Scholar
- 8.Dwork, D., Ravi, S., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web. In: Proceedings of World Wide Web, vol. 10, pp. 613–622 (2001)Google Scholar
- 11.Geleijnse, G., Schedl, M., Knees, P.: The Quest for Ground Truth in Musical Artist Tagging in the Social Web Era. In: International Conference on Music Information Retrieval, vol. 8, pp. 525–530 (2007)Google Scholar