Bisociative Music Discovery and Recommendation

  • Sebastian Stober
  • Stefan Haun
  • Andreas Nürnberger
Open Access
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)


Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems. Motivated by the concept of bisociations, we propose ways to create an environment where such serendipitous recommendations become more likely. As application domain we focus on music recommendation using MusicGalaxy, an adaptive user-interface for exploring music collections. It leverages a non-linear multi-focus distortion technique that adaptively highlights related music tracks in a projection-based collection visualization depending on the current region of interest. While originally developed to alleviate the impact of inevitable projection errors, it can also adapt according to user-preferences. We discuss how using this technique beyond its original purpose can create distortions of the visualization that facilitate bisociative music discovery.


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Authors and Affiliations

  • Sebastian Stober
    • 1
  • Stefan Haun
    • 1
  • Andreas Nürnberger
    • 1
  1. 1.Data & Knowledge Engineering Group, Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgMagdeburgGermany

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