We study the problem of constructing personalized playlists for users of music streaming services, in our case Spotify.
Categories for User Modeling
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...
A new approach to calibrating recommendations to user interests. Users’ interests are multi-faceted and representing different aspects of users’ interest in their recommendations is an important factor for recommender systems....
TL;DR: Survival analysis provides a framework to reason about time-to-event data; at Spotify, for example, we use it to understand and predict the way users might engage with Spotify in the future. In this work, we bring temporal-difference learning, a central idea in reinforcement learning, to survival analysis. We develop a new algorithm that trains a survival model from sequential data by leveraging a temporal consistency condition, and show that it outperforms direct regression on observed outcomes......
“Variety is the spice of life”, as the saying attributed to poet William Cowper goes. People crave heterogeneity and avoid... 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
What makes a particular podcast broadly engaging? As a media form, podcasting is new enough that such questions are only... 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
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
Algorithmically generated recommendations power and shape the bulk of music consumption on music streaming platforms. The ability to shift consumption... View Article
Music is such a core part of culture and everyday experience that it has long been believed to be connected... View Article
On Spotify, people are spoiled for choice: there are millions of songs by millions of artists that they can listen... View Article
Going back to the earliest forms of recorded music, technology has made it progressively easier for countries around the world... View Article
Because Spotify offers both music and podcast content on the same platform, we have a unique view into people’s audio... View Article
“What kind of music do you like?” When getting to know someone, music is often one of the very first... View Article
Given the overwhelming choices faced by users on what to watch, read and listen to online, recommender systems play a... View Article
If you’re a Spotify user, you may be familiar with Discover Weekly—your weekly, personalized playlist of music you’ve never heard... View Article