On an artist's profile page, music streaming services frequently recommend a ranked list of "similar artists" that fans also liked. However, implementing such a feature is challenging for new artists, for which usage data on the service (e.g. streams or likes) is not yet available. In this paper, we model this cold start similar artists ranking problem as a link prediction task in a directed and attributed graph, connecting artists to their top-k most similar neighbors and incorporating side musical information. Then, we leverage a graph autoencoder architecture to learn node embedding representations from this graph, and to automatically rank the top-k most similar neighbors of new artists using a gravity-inspired mechanism. We empirically show the flexibility and the effectiveness of our framework, by addressing a real-world cold start similar artists ranking problem on a global music streaming service. Along with this paper, we also publicly release our source code as well as the industrial graph data from our experiments.

本文提出一种基于图自编码器的冷启动相似音乐家排序解决方案，并将其应用于全球音乐流媒体服务的实际问题中。该方案解决了对于没有提供使用数据的新音乐家，如何实现在艺术家页面上推荐类似音乐家的问题，并展示了该方案的灵活性和效果。

基于启发式引力图自编码器的冷启动相似艺术家排名