Artist recommendation plays a vital role in the artist domain. Accurate recommendation can help avoid ineffective searches and acquire comprehensive knowledge regarding relationships among artists. However, existing studies mainly focus on artists themselves or artistic works. They are incapable of exploring the relationships among artists in an effective way. In this paper, we study the problem of artist recommendation for the first time. We propose a artist dataset to analyze the similarity

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... lationship from spatial and temporal aspects between artists. Specifically, based on the dataset, we propose a self-supervised learning approach to construct the artist graph. To incorporate the learned graph into existing models, we propose a novel network, SSAR-GNN for recommendation. SSAR-GNN applies a simplified Graph Convolution Network (GCN) on the artist graph to enrich the representation of each artist. Experimental results on the dataset show the effectiveness of our proposed method SSAR-GNN in terms of accuracy.