Mutual proximity graphs for improved reachability in music recommendation

Arthur Flexer, Jeff Stevens
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
more » ... ive to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness. ARTICLE HISTORY
doi:10.1080/09298215.2017.1354891 pmid:29348779 pmcid:PMC5750815 fatcat:w4rwguirxnd3tkoggnyriqm3ee