Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
mHashup: fast visual music discovery via locality sensitive hashing
ACM SIGGRAPH 2008 new tech demos
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IEEE MultiMedia
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Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
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Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
MusicGalaxy: a multi-focus zoomable interface for multi-facet exploration of music collections
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Recomindation: new functions for augmented memories
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Towards user-adaptive structuring and organization of music collections
AMR'08 Proceedings of the 6th international conference on Adaptive Multimedia Retrieval: identifying, Summarizing, and Recommending Image and Music
Similarity adaptation in an exploratory retrieval scenario
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Selecting the links in bisonets generated from document collections
Bisociative Knowledge Discovery
Bisociative Knowledge Discovery
Applications and evaluation: overview
Bisociative Knowledge Discovery
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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.