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The artist similarity quest has become a crucial subject in social andscientific contexts. Modern research solutions facilitate music discoveryaccording to user tastes. However, defining similarity among artists mayinvolve several aspects, even related to a subjective perspective, and it oftenaffects a recommendation. This paper presents GATSY, a recommendation systembuilt upon graph attention networks and driven by a clusterized embedding ofartists. The proposed framework takes advantage of a graph topology of theinput data to achieve outstanding performance results without relying heavilyon hand-crafted features. This flexibility allows us to introduce fictitiousartists in a music dataset, create bridges to previously unrelated artists, andget recommendations conditioned by possibly heterogeneous sources. Experimentalresults prove the effectiveness of the proposed method with respect tostate-of-the-art solutions.

GATSY: Graph Attention Network for Music Artist Similarity