mlsanityData to 14 Oct 2026

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

arXiv 2401.02827 ↗First posted 5 Jan 2024cs.IR cs.LGcited by 5

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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.

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

11 of 24 references.

References · 11

  1. 2017Billion-scale similarity search with GPUscited by 2,497
  2. 2019MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendationcited by 121
  3. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  4. 2017Current Challenges and Visions in Music Recommender Systems Researchcited by 42
  5. 2020Carousel Personalization in Music Streaming Apps with Contextual Banditscited by 26
  6. 2021A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Appscited by 21
  7. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  8. 2021Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoderscited by 12
  9. 2022Flow Moods: Recommending Music by Moods on Deezercited by 11
  10. 2023Track Mix Generation on Music Streaming Services using Transformerscited by 6
  11. 2021What is fair? Exploring the artists' perspective on the fairness of music streaming platformscited by 5

Built on by

Cited by year (2026 to 14 Oct)

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

5 papers.

  1. 2024Optimal Baseline Corrections for Off-Policy Contextual Banditscited by 13
  2. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 12
  3. 2024Multi-Objective Recommendation via Multivariate Policy Learningcited by 10
  4. 2025Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Samplingcited by 3
  5. 2026Music Playlist Captioning at Scale with Large Language Modelscited by 1