Search & Recommendations

Search & recommendations research at Spotify focuses on identifying ways to provide users with seamless access to their favorite audio content, from music to podcasts, and to help them explore their taste. A big focus is on building highly effective, personalized and interactive models that exploit contextual information and historical user interactions. Our research interests include large-scale recommendation algorithms for music and more generally audio discovery, algorithms for audio search through voice and text, query analysis for effective query suggestion, query completion and search assistance, multilingual information retrieval for voice search, and ranking algorithm for revenue and music ads.

Latest Search & Recommendations Publications

May 2024 | Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

Structural Podcast Content Modeling with Generalizability

Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou

May 2024 | The Web Conference (GFM workshop)

Towards Graph Foundation Models for Personalization

Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas

Other Research Areas