“Just The Way You Are”: Linking Music Listening on Spotify and Personality


Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.


September 2020 | RecSys

Contextual and Sequential User Embeddings for Large-Scale Music Recommendation

Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas

September 2020 | RecSys

Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions

Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas

September 2020 | RecSys

Investigating Listeners’ Responses to Divergent Recommendations

Rishabh Mehrotra, Chirag Shah, Benjamin Carterette