Artist Similarity Based on Heterogeneous Graph Neural Networks

Abstract

Music streaming platforms rely on recommending similar artists to maintain user engagement, with artists benefiting from these suggestions to boost their popularity. Another important feature is music information retrieval, allowing users to explore new content. In both scenarios, performance depends on how to compute the similarity between musical content. This is a challenging process since musical data is inherently multimodal, containing textual and audio data. We propose a novel graph-based artist representation that integrates audio, lyrics features, and artist relations. Thus, a multimodal representation on a heterogeneous graph is proposed, along with a network regularization process followed by a GNN model to aggregate multimodal information into a more robust unified representation. The proposed method explores this final multimodal representation for the task of artist similarity as a link prediction problem. Our method introduces a new importance matrix to emphasize related artists in this multimodal space. We compare our approach with other strong baselines based on combining input features, importance matrix construction, and GNN models. Experimental results highlight the superiority of multimodal representation through the transfer learning process and the value of the importance matrix in enhancing GNN models for artist similarity.