Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid approach to computing similarity between artists using graph neural networks trained with triplet loss. The novelty of using a graph neural network architecture is to combine the topology of a graph of artist connections with content features to embed artists into a vector space that encodes similarity. To evaluate the proposed method, we compile the new OLGA dataset, which contains artist similarities from AllMusic, together with content features from AcousticBrainz. With 17,673 artists, this is the largest academic artist similarity dataset that includes content-based features to date. Moreover, we also showcase the scalability of our approach by experimenting with a much larger proprietary dataset. Results show the superiority of the proposed approach over current state-of-the-art methods for music similarity. Finally, we hope that the OLGA dataset will facilitate research on data-driven models for artist similarity.

中文翻译：

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艺术家与图神经网络的相似性

艺术家的相似性在组织、理解以及随后促进发现大量音乐方面发挥着重要作用。在本文中，我们提出了一种混合方法，使用经过三元组损失训练的图神经网络来计算艺术家之间的相似度。使用图神经网络架构的新颖之处在于将艺术家联系图的拓扑结构与内容特征相结合，将艺术家嵌入到对相似性进行编码的向量空间中。为了评估所提出的方法，我们编译了新的 OLGA 数据集，其中包含来自 AllMusic 的艺术家相似性以及来自 AcousticBrainz 的内容特征。拥有 17,673 位艺术家，这是迄今为止最大的学术艺术家相似性数据集，其中包括基于内容的特征。而且，我们还通过试验更大的专有数据集来展示我们方法的可扩展性。结果表明，所提出的方法优于当前最先进的音乐相似性方法。最后，我们希望 OLGA 数据集将促进对艺术家相似性数据驱动模型的研究。