GATSY: Graph Attention Network for Music Artist Similarity

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.

@article{francesco2025_2311.00635,
  title={ GATSY: Graph Attention Network for Music Artist Similarity },
  author={ Andrea Giuseppe Di Francesco and Giuliano Giampietro and Indro Spinelli and Danilo Comminiello },
  journal={arXiv preprint arXiv:2311.00635},
  year={ 2025 }
}