Author Archives for Spotify Engineering

Users’ interests are multi-faceted: recommendation models should be too

February 22, 2023 10:58 am Published by Spotify Engineering Comments Off

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....

Survival Analysis Meets Reinforcement Learning

November 25, 2022 10:08 am Published by Spotify Engineering Comments Off

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......

Spotify’s Contributions to NeurIPS 2022

November 24, 2022 11:08 am Published by Spotify Engineering Comments Off

Cutting-edge research in Machine Learning, Language Technologies, User Modeling, Audio Intelligence, Search and Recommender Systems are some of the key areas we feel incredibly enthusiastic about at Spotify...

Can we correctly attribute changes among many possible causes when unobserved confounders are present?

November 24, 2022 11:06 am Published by Spotify Engineering Comments Off

There are many A/B tests we might like to run, but which are too technically challenging, risky in terms of user impact or even impossible to perform. For instance, in the classic example of whether smoking causes lung cancer, forcing a randomly selected group of people to smoke is unethical if we believe it might damage their health. In the context of technology companies, if we want to understand if app crashes cause users to churn, we would have to randomly select a subgroup of users and crash their apps on purpose – not something we would want to consider given we do not want to break their trust....