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
The 15th International ACM Conference on Recommender Systems (RecSys 2021) starts this week, and Spotify is excited to be a... View Article
Tl;dr: Music and podcast search are different, and a carefully designed multi-task model will be essential for search in modern... View Article
An increasingly larger proportion of users rely on recommendation systems to pro-actively serve them recommendations based on diverse user needs... View Article
The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) starts this week, and Spotify... View Article
Using a music listening dataset from Spotify, we observe that consumption from the recent past and session-level contextual variables (such... View Article
At Spotify, we invest into designing recommendation algorithms that allow users to explore the music space more effectively. Recent findings... View Article
Search plays an important role in surfacing content in front of users. Typical of most search and recommender systems, there... View Article
Algorithmically generated recommendations power and shape the bulk of music consumption on music streaming platforms. The ability to shift consumption... View Article
On Spotify, people are spoiled for choice: there are millions of songs by millions of artists that they can listen... View Article
In 2018, Spotify helped organize the RecSys Challenge 2018, a data science research challenge focused on music recommendation, specifically the... View Article
The 14th ACM Conference on Recommender Systems (RecSys 2020) begins this week, and we are excited to be a Platinum... View Article