论文标题
部分可观测时空混沌系统的无模型预测
Flow Moods: Recommending Music by Moods on Deezer
论文作者
论文摘要
音乐流媒体服务Deezer广泛依赖其流程算法，该算法生成了个性化的无线电播放列表，以帮助用户发现音乐内容。尽管如此，尽管过去几年中有很有希望的结果，但在提供建议时，流动却忽略了用户的心情。在本文中，我们提出了Flow Moods，这是一个改进的流程，以解决此限制。流动情绪利用专业音乐策展人的协作过滤，音频内容分析和情绪注释，以大规模生成个性化的情绪特定播放列表。我们详细介绍了该系统在Deezer上的动机，发展和部署。自2021年发行以来，Flow Moods每天都向数百万用户推荐音乐。
The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years, Flow used to ignore the moods of users when providing recommendations. In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation. Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale. We detail the motivations, the development, and the deployment of this system on Deezer. Since its release in 2021, Flow Moods has been recommending music by moods to millions of users every day.
