Music Playlist Captioning at Scale with Large Language Models

2026-06-21

AI Summary

This work addresses the lack of interpretability in personalized playlists on music streaming platforms by deploying, for the first time at industrial scale, a large language model (LLM)-based automatic captioning system within Deezer’s Daily Mix recommendation service. The proposed approach integrates multi-source heterogeneous data and leverages a controllable generation mechanism to produce semantically rich and personalized natural language descriptions. Following deployment, the system yielded significant gains in user engagement, demonstrating that semantic explanations play a pivotal role in enhancing both the interpretability of recommendations and overall user experience. This study establishes an effective paradigm for controllable text generation with LLMs in real-world applications, offering practical insights into bridging the gap between algorithmic personalization and human-understandable justifications.

Abstract

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.

Problem

• playlist captioning
• music streaming
• personalized recommendation
• natural language generation
• large language models

Innovation

• playlist captioning
• large language models
• personalized recommendation
• controlled text generation
• user engagement