Personalizing Agentic AI to Users' Musical Tastes with Scalable Preference Optimization
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Profile-aware LLM-as-a-Judge for Podcasts: A Better Middle Ground Between Offline Metrics and A/B Tests
Semantic IDs for Generative Search and Recommendation
Latest Publications
We publish research papers and present our work in a wide range of venues.
More publicationsLLARK : A Multimodal Instruction-Following Language Model for Music
Joshua Gardner, Simon Durand, Daniel Stoller, Rachel Bittner
Calibrated Recommendations with Contextual Bandits
Diego Feijer, Himan Abdollahpouri, Sanket Gupta, Alexander Clare, Yuxiao Wen, Todd Wasson, Maria Dimakopoulou, Zahra Nazari, Kyle Kretschman, Mounia Lalmas
Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge
Francesco Fabbri, Gustavo Penha, Edoardo D'Amico, Alice Wang, Marco De Nadai, Jackie Doremus, Paul Gigioli, Andreas Damianou, Oskar Stål, Mounia Lalmas
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.
We are looking for pioneers to join us in all research areas
We're expanding knowledge of audio and video technology every day, sharing open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
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