From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Hypothesis-Driven Shelf Generation for Personalised Recommendation
Who Are We Recommending To? Recommender Systems in the Agentic Web
Contexts, Conversations & Connections: Spotify Research at RecSys 2026
Latest Publications
We publish research papers and present our work in a wide range of venues.
More publicationsWho Are We Recommending To? Recommender Systems in the Agentic Web
Himan Abdollahpouri, Kyle Kretschman, Sai Srivatsa Ravindranath, Jackie Doremus, Mounia Lalmas
Hypothesis-Driven Shelf Generation for Personalised Recommendation
Aleksandr V. Petrov, Tarun Chillara, Matthew D. Moellman, Lucas de Haas, Yabai Song, Alina Susoykina, Melissa Crawford, Gabriel Negash, Erik Franco, Tasnim Rahman, Binal Jhaveri, Shubham Bansal, Hugues Bouchard, Roberto Mirizzi, Mounia Lalmas, Aloïs Gruson
From Habits to Discovery: Deploying LLMs for Personalized Generative Recommendations at Spotify
Edoardo D'Amico, Marco De Nadai, Praveen Chandar et al
Research Areas
How do we create more personalized experiences? What can we learn about listeners on how they use written language? How do we optimize testing methodologies? Explore all our research areas below.
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