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.
翻译：音乐流传服务 Deezer 大量依赖其流动算法,它生成了个性化的无线电式歌曲播放列表,以帮助用户发现音乐内容。尽管过去几年取得了令人乐观的成果,但流却在提供建议时忽视了用户的情绪。在本文中,我们介绍了流动模式,这是针对这一限制的改进版的流体。 流动模式利用了专业音乐馆的合作过滤、音频内容分析以及情绪说明,产生了个性化的情绪特定播放列表。我们详细介绍了Deezer的动机、发展和配置。自2021年发布以来,流动模式每天向数百万用户推荐以情绪表达音乐。
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