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Music Artist Similarity Aggregation

  • Brandeis MarshallEmail author
Chapter
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Abstract

Through user accounts, music recommendations are refined by user-supplied genres and artists preferences. Music recommendation is further complicated by multiple genre artists, artist collaborations and artist similarity identification. We focus primarily on artist similarity in which we propose a rank fusion solution. We aggregate the most similar artist ranking from Idiomag, Last.fm and Echo Nest Web APIs. Each Web API and resulting ranked list using a rank fusion algorithm has a different method of relating their artists. Through an experimental evaluation of nearly 500 artist queries, we first identify a ground truth for artist similarity for comparison to five rank fusion algorithms and conduct a performance analysis to discern how well relevant information can be retrieved from biased multiple sources. By understanding this overlap, we can more easily isolate the multiple genre artists and artist collaborations.

Keywords

Ground Truth Lower Precision Rank Position Music Genre Music Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Vienna 2012

Authors and Affiliations

  1. 1.Computer and Information TechnologyPurdue UniversityWest LafayetteUSA

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