mlsanityData to 14 Oct 2026

A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services

arXiv 2304.09061 ↗First posted 12 Apr 2023cs.IR cs.LG cs.SDcited by 7

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Abstract (arXiv)

Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their musical characteristics and matching user preferences remains a challenging task, commonly referred to as Automatic Playlist Continuation (APC). Besides, while these services often need to select the best songs to recommend in real-time and among large catalogs with millions of candidates, recent research on APC mainly focused on models with few scalability guarantees and evaluated on relatively small datasets. In this paper, we introduce a general framework to build scalable yet effective APC models for large-scale applications. Based on a represent-then-aggregate strategy, it ensures scalability by design while remaining flexible enough to incorporate a wide range of representation learning and sequence modeling techniques, e.g., based on Transformers. We demonstrate the relevance of this framework through in-depth experimental validation on Spotify's Million Playlist Dataset (MPD), the largest public dataset for APC. We also describe how, in 2022, we successfully leveraged this framework to improve APC in production on Deezer. We report results from a large-scale online A/B test on this service, emphasizing the practical impact of our approach in such a real-world application.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 25 of its 64 references that are in the map. First posted on arXiv 12 Apr 2023.

Builds on

25 of 64 references.

References · 25

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13 more
  1. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 112
  2. 2017Current Challenges and Visions in Music Recommender Systems Researchcited by 42
  3. 2018Evaluation of Session-based Recommendation Algorithmscited by 37
  4. 2016Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendationcited by 30
  5. 2020Carousel Personalization in Music Streaming Apps with Contextual Banditscited by 26
  6. 2019The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Studycited by 24
  7. 2021A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Appscited by 21
  8. 2019Empirical Analysis of Session-Based Recommendation Algorithmscited by 16
  9. 2021Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoderscited by 12
  10. 2022Flow Moods: Recommending Music by Moods on Deezercited by 11
  11. 2018An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuationcited by 9
  12. 2021Melon Playlist Dataset: a public dataset for audio-based playlist generation and music taggingcited by 5
  13. 2019Automatic playlist continuation using a hybrid recommender system combining features from text and audiocited by 1

Built on by

Cited by year (2026 to 14 Oct)

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

7 papers.

  1. 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
  2. 2023Track Mix Generation on Music Streaming Services using Transformerscited by 6
  3. 2023On the Consistency of Average Embeddings for Item Recommendationcited by 5
  4. 2024Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendationcited by 4
  5. 2025Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Samplingcited by 3
  6. 2026Music Playlist Captioning at Scale with Large Language Modelscited by 1
  7. 2026Privacy in Personalized AI Is a System Property, Not Just a Model Propertycited by 0