Flow Moods: Recommending Music by Moods on Deezer

Théo Bontempelli, Benjamin Chapus, François Rigaud, Mathieu Morlon, Marin Lorant +1 more
7/15/2022
cs.IRcs.LG

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Emotion-Aware Music Recommendation System: Enhancing User Experience Through Real-Time Emotional Context

83% match2023

Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation

82% match1 repo32025

Emotion Detection and Music Recommendation System

80% match2025

Related Rhythms: Recommendation System To Discover Music You May Like

80% match2023

AffectMachine-Classical: A novel system for generating affective classical music

80% match2023

AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges

80% match2023

Cite this paper

@article{bontempelli2022flow,
  title  = {Flow Moods: Recommending Music by Moods on Deezer},
  author = {Théo Bontempelli and Benjamin Chapus and François Rigaud and Mathieu Morlon and Marin Lorant and Guillaume Salha-Galvan},
  year   = {2022},
  eprint = {2207.11229},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.11229v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2207.11229.svg)](https://codeofpaper.com/paper/2207.11229)

Discussion