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

October 2023 | CIKM

Exploiting Sequential Music Preferences via Optimisation-Based Sequencing

Dmitrii Moor, Yi Yuan, Rishabh Mehrotra, Zhenwen Dai, Mounia Lalmas

July 2023 | KDD

Impatient Bandits: Optimizing for the Long-Term Without Delay

Thomas McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek

July 2023 | KDD

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Federico Tomasi, Joseph Cauteruccio, Surya Kanoria, Kamil Ciosek, Matteo Rinaldi, Zhenwen Dai

Other Research Areas