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Mutual proximity graphs for improved reachability in music recommendation
2017
Journal of New Music Research
This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. 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, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an
doi:10.1080/09298215.2017.1354891
pmid:29348779
pmcid:PMC5750815
fatcat:w4rwguirxnd3tkoggnyriqm3ee