Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

TL;DR

This paper introduces a gravity-inspired graph autoencoder approach to rank similar artists in music streaming services, effectively addressing the cold start problem for new artists by leveraging graph structure and musical features.

Contribution

It proposes a novel graph autoencoder framework with a gravity-inspired ranking mechanism for cold start artist similarity, incorporating musical attributes and graph structure.

Findings

Effective in real-world music streaming data

Outperforms baseline methods in artist similarity ranking

Provides publicly available code and data for reproducibility

Abstract

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.