Bridging Search and Recommendation with Generative Retrieval
October 21, 2024 9:35 amSearch engines and recommendation systems use different signals to represent users and catalog items, catering to their distinct tasks..
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..
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 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.
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...
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.
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.
A large number of new podcasts are launched every month on Spotify and other online media platforms. In this work,... View Article
Here at Spotify, we are highly dedicated to cutting-edge research in various areas in Machine Learning, User Modeling, Personalization, and... View Article
“Variety is the spice of life”, as the saying attributed to poet William Cowper goes. People crave heterogeneity and avoid... View Article
Song lyrics make an important contribution to the musical experience, providing us with rich stories and messages that artists want... View Article
Podcasting as a medium is growing exponentially, with hundreds of thousands of shows available in genres from comedy to news... View Article
Recommendation engines support most modern digital platforms, allowing users to navigate vast databases of products in Amazon, homes in AirBnB,... View Article
Existing recommender systems are limited in the ability to help us grow and understand our personal music preferences Recommender systems... View Article
Music recommendation systems at Spotify are built on models of users and items. They often rely on past user interactions... View Article
One question we spend a lot of time thinking about at Spotify is how to help creators build larger audiences,... View Article
Personalization services at Spotify rely on learning meaningful representations of tracks and users to surface apt recommendations to users in... View Article
Quality user feedback is important for making good music recommendations. Recommending the right music to a user at the right... View Article