Personalization services at Spotify rely on learning meaningful representations of tracks and users to surface apt recommendations to users in... View Article
Machine learning practitioners regularly have to select the best model to deploy in production. At Spotify, for instance, we might... View Article
At Spotify, we invest into designing recommendation algorithms that allow users to explore the music space more effectively. Recent findings... View Article
On online platforms such as Spotify, users have access to large collections of content. Most of the time, these collections... View Article
Algorithmically generated recommendations power and shape the bulk of music consumption on music streaming platforms. The ability to shift consumption... View Article
Topic models are useful tools for the statistical analysis of data as well as learning a compact representation of co-occurring... View Article
The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020) begins this week, and we are excited to be a... 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
Making sense of music by extracting and analyzing individual instruments in a song Source Separation at Spotify At the end... View Article
Spotify was out in full force at RecSys in Copenhagen. The ACM Conference on Recommender Systems (RecSys) is the premier... View Article
Given the overwhelming choices faced by users on what to watch, read and listen to online, recommender systems play a... View Article
Two-sided marketplaces act as intermediaries that help facilitate economic interaction between two sets of agents; for example, consumers and suppliers,... View Article