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......
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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
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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