Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), offers new opportunities to connect listeners...
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.
We study the problem of constructing personalized playlists for users of music streaming services, in our case Spotify.
Spotify’s search system plays a vital role in helping users explore the catalog and discover new content.
Every day, music is enjoyed, created, and discovered by billions of people around the globe – and yet, existing AI systems largely struggle to model the nuances that make music different from other forms of audio.
7am I usually wake up around 7am and have breakfast with my husband before he goes to work. We chat... View Article
Existing spoken Language Identification (SLI) solutions focus on detecting languages from short audio clips. Podcast audio, on the other hand, poses peculiar challenges...
TL;DR: On online platforms such as Spotify, recommender systems are increasingly tasked with improving users’ long-term satisfaction...
Reinforcement learning (RL) is an established tool for sequential decision making. In this work, we apply RL to solve an automatic music playlist generation problem...
Launching a radical change to a habitual feature, used by millions every day, presents an interesting challenge.
7am I usually wake up around 7am – I’m not a morning person by nature, but I have two young... View Article
Allowing users to discover new entities such as books, music, and movies is an important goal for online platforms. This can be achieved for example by recommending entities that the user has not yet interacted with. Another way users can find new entities is by exploring the catalog with the search system.
Answering causal questions with machine learning algorithms is a challenging yet critical task.
Quantifying cause and effect relationships is of fundamental importance in many fields, from medicine to economics. The gold standard solution to this problem is to conduct randomised controlled trials, or A/B tests.
Understanding cause and effect relationships in Spotify data to inform decision-making is crucial for best serving Spotify’s users and the company.
Recommender systems typically look to users' past consumption to predict what they may want next. In practice, this approach tends to work best when what the user wants is similar to what they have consumed recently, and when it is relatively easy for that person to evaluate new items.