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OFAI-TR-2015-02 ( 235kB PDF file)

The impact of hubness on music recommendation

Arthur Flexer

We review the impact of hubness, a general problem of machine learning in high-dimensional spaces, on music recommendation. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists. After reviewing the theory concerning the hubness phenomenon, we present methods which are able to decisively diminish hubness and its adverse effects in music and general multimedia datasets.

Keywords: hubness, music information retrieval, music recommendation, curse of dimensionality

Citation: Flexer A.: The impact of hubness on music recommendation, Machine Learning for Music Discovery Workshop at the 32nd International Conference on Machine Learning, Lille, France, 2015.