用大模型自动生成音乐播放列表的自然语言描述，提升用户理解与参与度。
Music Playlist Captioning at Scale with Large Language Models
- 基于大模型从多源数据生成受控的自然语言描述
- 系统上线后显著提升数百万用户的使用参与度
- 适合对个性化推荐和用户体验优化感兴趣的团队
音乐流媒体服务如Deezer常为用户推荐个性化播放列表。播放列表描述（Playlist Captioning）旨在用自然语言解释推荐内容，帮助用户理解背后逻辑，但大规模实现仍具挑战。本文介绍2025年在Deezer部署的自动播放列表描述系统，利用大语言模型（LLMs）从多样化数据源中可控地生成描述性文案，现已支撑每日混搭（Daily Mix）功能，服务数百万用户。该系统显著提升了用户参与度，凸显了即使推荐内容不变，语义化表述也能深刻影响用户感知，改善在线个性化体验。
原文摘要 · Abstract (English)
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.
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