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

March 2023 | Frontier on Big Data: Recommender Systems

A Survey on Multi-objective Recommender Systems

Dietmar Jannach and Himan Abdollahpouri

March 2023 | Intelligent User Interfaces (IUI)

Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems

Yu Liang, Aditya Ponnada, Paul Lamere, Nediyana Daskalova

February 2023 | WSDM

Calibrated Recommendations as a Minimum-Cost Flow Problem

Himan Abdollahpouri, Zahra Nazari, Alex Gain,Clay Giibson, Maria Dimakopoulou, Jesse Anderton, Benjamin Carterette, Mounia Lalmas, Tony Jebara

Other Research Areas