The investigation of the similarity between artists and music is crucial in music retrieval and recommendation, and addressing the challenge of the long-tail phenomenon is increasingly important. This paper proposes a Long-Tail Friendly Representation Framework (LTFRF) that utilizes neural networks to model the similarity relationship. Our approach integrates music, user, metadata, and relationship data into a unified metric learning framework, and employs a meta-consistency relationship as a regular term to introduce the Multi-Relationship Loss. Compared to the Graph Neural Network (GNN), our proposed framework improves the representation performance in long-tail scenarios, which are characterized by sparse relationships between artists and music. We conduct experiments and analysis on the AllMusic dataset, and the results demonstrate that our framework provides a favorable generalization of artist and music representation. Specifically, on similar artist/music recommendation tasks, the LTFRF outperforms the baseline by 9.69%/19.42% in Hit Ratio@10, and in long-tail cases, the framework achieves 11.05%/14.14% higher than the baseline in Consistent@10.

翻译：艺术家与音乐相似性的研究在音乐检索与推荐中至关重要，而应对长尾现象的挑战日益凸显。本文提出一种长尾友好表示框架（LTFRF），利用神经网络对相似性关系进行建模。该方法将音乐、用户、元数据及关系数据整合至统一度量学习框架，并采用元一致性关系作为正则项引入多关系损失。与图神经网络（GNN）相比，所提框架在艺术家与音乐关系稀疏的长尾场景中提升了表示性能。我们在AllMusic数据集上进行实验与分析，结果表明该框架对艺术家与音乐表示具有良好的泛化能力。具体而言，在相似艺术家/音乐推荐任务中，LTFRF在Hit Ratio@10指标上分别比基线提升9.69%/19.42%；在长尾场景下，该框架在Consistent@10指标上比基线分别提升11.05%/14.14%。