DJ-Running: An Emotion-based System for Recommending Spotify Songs to Runners

P. Álvarez, A. Guiu, J. Beltrán, J. García de Quirós, S. Baldassarri

Abstract

People that practice running use to listen to music during their training sessions. Music can have a positive influence on runners’ motivation and performance, but it requires selecting the most suitable song at each moment. Most of the music recommendation systems combine users’ preferences and context-aware factors to predict the next song. In this paper, we include runners’ emotions as part of these decisions. This fact has forced us to emotionally annotate the songs available in the system, to monitor runners’ emotional state and to interpret these data in the recommendation algorithms. A new next-song recommendation system and a mobile application able to play the recommended music from the Spotify streaming service have been developed. The solution combines artificial intelligence techniques with Web service ecosystems, providing an innovative emotion-based approach.

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