Blog

Developing innovative products play an essential part of Spotify’s success...

Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), offers new opportunities to connect listeners...

Listeners often find it challenging to navigate long podcast episodes due to their long duration...

Search engines and recommendation systems use different signals to represent users and catalog items, catering to their distinct tasks..

tldr - The wide variety of reasons people listen to music include both individual motivations, like self-awareness and mood regulation, as well as social motivations, such as demonstrating belonging to a group or feeling connected to friends.

tldr – Music has always been intertwined with place and culture. Now that digital streaming has made it easier than ever to encounter music from all over the world, what role does geography play in people’s relationship to music?

Spotify's catalog includes millions of music tracks and podcasts and has recently expanded to Audiobooks. Personalizing this content to users requires our algorithms to “understand” user preferences as well as content relationships across all content types...

There are numerous scenarios where the short- and long-term consequences of an algorithm can differ..

Machine learning (ML) models are widely used on Spotify to provide users with a daily personalized listening experience.

The rise of on-demand music streaming supported by novel recommendation algorithms has transformed music listening...

TL;DR: Across a broad spectrum of product features, Spotify’s array of recommender systems play a pivotal role in tailoring personalized user experiences and helping creators grow.

We developed a Named Entity Disambiguation (NED) method to assist human curators in finding and correcting infrequent errors in a music catalog.