User Modeling

Serving the diverse needs of millions of users requires the development of technology to not only understand user tastes and interests, but also consider contextual preferences of the users to serve them relevant, timely and inspirational recommendations. The user modeling research at Spotify entails translating users’ in-app activities into human traits, interaction models, emotional understanding modules, and situational contexts, thereby uncovering our users’ individuality. To do this, we use a multidisciplinary scientific approach at the intersection of music psychology, behavioral analysis, and machine learning. This enables us to build datasets and user interaction models to create engaging and personalized experiences for our users, in a data-driven fashion based on users’ interactions and feedback signals.

Latest User Modeling Publications

June 2022 | ICWSM

Time after Time: Longitudinal Trends in Nostalgic Listening

Clara Hanson, Jesse Anderton, Samuel F. Way, Ian Anderson, Scott Wolf, Alice Wang

June 2022 | ICWSM

The Dynamics of Exploration on Spotify

Lillio Mok, Samuel F. Way, Lucas Maystre, Ashton Anderson

April 2022 | The Web Conference (WWW)

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas

Other Research Areas