  The artist similarity quest has become a crucial subject in social and
scientific contexts. Modern research solutions facilitate music discovery
according to user tastes. However, defining similarity among artists may
involve several aspects, even related to a subjective perspective, and it often
affects a recommendation. This paper presents GATSY, a recommendation system
built upon graph attention networks and driven by a clusterized embedding of
artists. The proposed framework takes advantage of a graph topology of the
input data to achieve outstanding performance results without relying heavily
on hand-crafted features. This flexibility allows us to introduce fictitious
artists in a music dataset, create bridges to previously unrelated artists, and
get recommendations conditioned by possibly heterogeneous sources. Experimental
results prove the effectiveness of the proposed method with respect to
state-of-the-art solutions.
