  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.

Information Retrieval;Machine Learning