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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. Through an experimental evaluation of 300 artist queries, we compare five rank fusion algorithms and how each fusion method could impact the retrieval of established, new or cross-genre music artists.