mlsanityData to 7 Oct 2026

Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

arXiv 2108.01053 ↗First posted 2 Aug 2021cs.LG cs.IR cs.SIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 29 of its 71 references that are in the map. First posted on arXiv 2 Aug 2021.

Builds on

29 of 71 references.

References · 29

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17 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 3; 2025: 1; 2026: 2.

12 papers.