# Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

arXiv: https://arxiv.org/abs/2108.01053 · HTML: https://mlsanity.com/paper/2108.01053/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 71 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Constrained Graph Variational Autoencoders for Molecule Design](https://mlsanity.com/paper/1805.09076/) (2018)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Graphite: Iterative Generative Modeling of Graphs](https://mlsanity.com/paper/1803.10459/) (2018)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders](https://mlsanity.com/paper/1809.02630/) (2018)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Semi-Implicit Graph Variational Auto-Encoders](https://mlsanity.com/paper/1908.07078/) (2019)
- [Current Challenges and Visions in Music Recommender Systems Research](https://mlsanity.com/paper/1710.03208/) (2017)
- [Inductive Link Prediction for Nodes Having Only Attribute Information](https://mlsanity.com/paper/2007.08053/) (2020)
- [Gravity-Inspired Graph Autoencoders for Directed Link Prediction](https://mlsanity.com/paper/1905.09570/) (2019)
- [Carousel Personalization in Music Streaming Apps with Contextual Bandits](https://mlsanity.com/paper/2009.06546/) (2020)
- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Simple and Effective Graph Autoencoders with One-Hop Linear Models](https://mlsanity.com/paper/2001.07614/) (2020)
- [Music Mood Detection Based On Audio And Lyrics With Deep Neural Net](https://mlsanity.com/paper/1809.07276/) (2018)
- [A Degeneracy Framework for Scalable Graph Autoencoders](https://mlsanity.com/paper/1902.08813/) (2019)
- [Modeling the Music Genre Perception across Language-Bound Cultures](https://mlsanity.com/paper/2010.06325/) (2020)
- [Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks](https://mlsanity.com/paper/1912.12398/) (2019)

## Built on by (most cited first, 12 of 12)

- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [Flow Moods: Recommending Music by Moods on Deezer](https://mlsanity.com/paper/2207.11229/) (2022)
- [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023)
- [Let's Get It Started: Fostering the Discoverability of New Releases on Deezer](https://mlsanity.com/paper/2401.02827/) (2024)
- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)
- [IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu](https://mlsanity.com/paper/2603.01590/) (2026)
- [ALCAP: Alignment-Augmented Music Captioner](https://mlsanity.com/paper/2212.10901/) (2022)
- [Of Spiky SVDs and Music Recommendation](https://mlsanity.com/paper/2307.01212/) (2023)
- [Music Playlist Captioning at Scale with Large Language Models](https://mlsanity.com/paper/2606.22460/) (2026)
- [Cold-start Recommendation by Personalized Embedding Region Elicitation](https://mlsanity.com/paper/2406.00973/) (2024)
- [Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks](https://mlsanity.com/paper/2409.09026/) (2024)
- [To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders](https://mlsanity.com/paper/2502.16724/) (2025)

## 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.