Chapter

Bisociative Knowledge Discovery

Volume 7250 of the series Lecture Notes in Computer Science pp 472-483

Open Access This content is freely available online to anyone, anywhere at any time.

Bisociative Music Discovery and Recommendation

  • Sebastian StoberAffiliated withCarnegie Mellon UniversityData & Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg
  • , Stefan HaunAffiliated withCarnegie Mellon UniversityData & Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg
  • , Andreas NürnbergerAffiliated withCarnegie Mellon UniversityData & Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg

Abstract

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.