CMS Seminar: Research at Deezer

Deezer is an international music streaming platform serving millions of users worldwide, with a continuously growing and diverse catalog. This setting provides a unique and valuable lens through which to study how humans interact with music over extended periods of time, enabling a broad range of research directions: from extracting information from audio signals and improving recommendation systems, to investigating the cultural practices surrounding music consumption. In this talk, we present Deezer's research ecosystem and survey the diverse topics that have been explored over the years.

Biography

Gabriel Meseguer-Brocal is a senior research scientist at Deezer with over 10 years of experience in the music industry. Before joining Deezer, he completed postdoctoral research at Centre National de la Recherche Scientifique (CNRS) in France. In 2020, he earned his Ph.D. in Computer Science, Telecommunications, and Electronics with a focus on the Sciences & Technologies of Music and Sound at IRCAM. His research interests include signal processing and deep learning techniques for music processing, with a focus on areas such as source separation, self-supervised learning, dataset creation, AI-detection, and multimodal analysis.

Bruno Sguerra is a senior research scientist at Deezer. His research focuses on understanding user behaviour in music streaming environments, with particular interest in patterns of repetition, discovery, search, and contextual listening. He develops user models for recommendation systems, drawing on behavioural and psychological frameworks such as the mere exposure effect and the modelling of uncertainty in user feedback. Bruno has been actively involved in the research community, serving as a meta-reviewer for The Web Conference (WWW) and as a reviewer for RecSys. He co-organised the RecSys 2026 Challenge on conversational recommender systems.

Yuexuan Kong was a PhD student at Deezer in collaboration with Centrale Nantes. Her research focused on self-supervised learning and representation learning for music information retrieval. She worked on designing musically informed loss functions and developing efficient training methods for compact models capable of encoding rich musical information in their representations.

Also available on Zoom: https://zoom.us/j/99433440421?pwd=ZWxCQXFZclRtbjNXa0s2K1Q2REVPZz09