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......
Categories for Machine Learning
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
Dynamic topic modeling is a well established tool for capturing the temporal dynamics of the topics of a corpus....
What is Speaker Diarization? Speaker diarization is the process of logging the timestamps of when various speakers take turns to talk...
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
Song lyrics make an important contribution to the musical experience, providing us with rich stories and messages that artists want... View Article
Music recommendation systems at Spotify are built on models of users and items. They often rely on past user interactions... View Article
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