# Let's Get It Started: Fostering the Discoverability of New Releases on Deezer

arXiv: https://arxiv.org/abs/2401.02827 · HTML: https://mlsanity.com/paper/2401.02827/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Current Challenges and Visions in Music Recommender Systems Research](https://mlsanity.com/paper/1710.03208/) (2017)
- [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)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Flow Moods: Recommending Music by Moods on Deezer](https://mlsanity.com/paper/2207.11229/) (2022)
- [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023)
- [What is fair? Exploring the artists' perspective on the fairness of music streaming platforms](https://mlsanity.com/paper/2106.02415/) (2021)

## Built on by (most cited first, 5 of 5)

- [Optimal Baseline Corrections for Off-Policy Contextual Bandits](https://mlsanity.com/paper/2405.05736/) (2024)
- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://mlsanity.com/paper/2402.18590/) (2024)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling](https://mlsanity.com/paper/2507.00518/) (2025)
- [Music Playlist Captioning at Scale with Large Language Models](https://mlsanity.com/paper/2606.22460/) (2026)

## Abstract (arXiv)

This paper presents our recent initiatives to foster the discoverability of new releases on the music streaming service Deezer. After introducing our search and recommendation features dedicated to new releases, we outline our shift from editorial to personalized release suggestions using cold start embeddings and contextual bandits. Backed by online experiments, we discuss the advantages of this shift in terms of recommendation quality and exposure of new releases on the service.