Adaptive User Modeling for Content-Based Music Retrieval

  • Kay Wolter
  • Christoph Bastuck
  • Daniel Gärtner
Conference paper

DOI: 10.1007/978-3-642-14758-6_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)
Cite this paper as:
Wolter K., Bastuck C., Gärtner D. (2010) Adaptive User Modeling for Content-Based Music Retrieval. In: Detyniecki M., Leiner U., Nürnberger A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kay Wolter
    • 1
  • Christoph Bastuck
    • 1
  • Daniel Gärtner
    • 1
  1. 1.Fraunhofer Institute for Digital Media TechnologyIlmenauGermany

Personalised recommendations