Understanding cause and effect relationships in Spotify data to inform decision-making is crucial for best serving Spotify’s users and the company.
Recommender systems typically look to users' past consumption to predict what they may want next. In practice, this approach tends to work best when what the user wants is similar to what they have consumed recently, and when it is relatively easy for that person to evaluate new items.
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......
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...
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....
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
“Variety is the spice of life”, as the saying attributed to poet William Cowper goes. People crave heterogeneity and avoid... View Article
Song lyrics make an important contribution to the musical experience, providing us with rich stories and messages that artists want... View Article
7.30am I usually get up at around 7.30am and start my day with a quick breakfast. I like to set to... 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
Existing recommender systems are limited in the ability to help us grow and understand our personal music preferences Recommender systems... View Article
Music recommendation systems at Spotify are built on models of users and items. They often rely on past user interactions... View Article
One question we spend a lot of time thinking about at Spotify is how to help creators build larger audiences,... View Article
6.15am I tend to wake up early because I like to get a headstart on the day. I jump straight in... View Article
Personalization services at Spotify rely on learning meaningful representations of tracks and users to surface apt recommendations to users in... View Article
Wikipedia is the largest online encyclopedia, and English Wikipedia is one of the most visited websites worldwide. With its number... View Article
Quality user feedback is important for making good music recommendations. Recommending the right music to a user at the right... View Article
The 15th International ACM Conference on Recommender Systems (RecSys 2021) starts this week, and Spotify is excited to be a... View Article
Tl;dr: Music and podcast search are different, and a carefully designed multi-task model will be essential for search in modern... View Article