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.

翻译：艺术家相似性搜索已成为社会与科学领域的重要课题。现代研究方案能根据用户偏好辅助音乐发现。然而，定义艺术家之间的相似性可能涉及多个维度，甚至包含主观视角因素，这常对推荐效果产生影响。本文提出GATSY——一种基于图注意力网络（Graph Attention Network）并采用艺术家聚类嵌入驱动的推荐系统。该框架利用输入数据的图拓扑结构，在不严重依赖手工特征的情况下实现卓越性能。这种灵活性使我们能够在音乐数据集中引入虚构艺术家，建立与先前无关艺术家的关联桥梁，并基于可能异构的数据源生成条件化推荐。实验结果表明，该方法相较于现有最优方案具有显著有效性。