# Music Playlist Captioning at Scale with Large Language Models

arXiv: https://arxiv.org/abs/2606.22460 · HTML: https://mlsanity.com/paper/2606.22460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Builds on (most cited first, 25 of 25)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [LP-MusicCaps: LLM-Based Pseudo Music Captioning](https://mlsanity.com/paper/2307.16372/) (2023)
- [Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning](https://mlsanity.com/paper/2308.11276/) (2023)
- [MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://mlsanity.com/paper/2309.08730/) (2023)
- [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)
- [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)
- [Towards Music Captioning: Generating Music Playlist Descriptions](https://mlsanity.com/paper/1608.04868/) (2016)
- [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023)
- [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023)
- [Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation](https://mlsanity.com/paper/2009.07755/) (2020)
- [Music Playlist Title Generation: A Machine-Translation Approach](https://mlsanity.com/paper/2110.07354/) (2021)
- [Let's Get It Started: Fostering the Discoverability of New Releases on Deezer](https://mlsanity.com/paper/2401.02827/) (2024)
- [Audio Based Disambiguation Of Music Genre Tags](https://mlsanity.com/paper/1809.07256/) (2018)
- [Music Playlist Title Generation Using Artist Information](https://mlsanity.com/paper/2301.08145/) (2023)
- [Rethinking Music Captioning with Music Metadata LLMs](https://mlsanity.com/paper/2602.03023/) (2026)
- [IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu](https://mlsanity.com/paper/2603.01590/) (2026)
- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)

## Built on by (most cited first, 1 of 1)

- [Privacy in Personalized AI Is a System Property, Not Just a Model Property](https://mlsanity.com/paper/2609.38289/) (2026)

## Abstract (arXiv)

Music streaming services such as Deezer often recommend personalized playlists to users. Playlist captioning, which involves describing these playlists in natural language, is essential for helping users understand the content behind each recommendation, yet remains challenging at scale. This paper presents the automatic playlist captioning system deployed on Deezer in 2025 to address this challenge. Leveraging recent advances in large language models (LLMs) to generate descriptive captions from diverse data sources in a controlled manner, this system now powers the Daily Mix feature, used by millions of users. This deployment has led to significant improvements in user engagement, highlighting how the semantic framing of an unchanged recommendation shapes user perception in online personalized experiences.