Personalizing Spotify Home with Machine Learning

Personalizing Spotify Home with Machine Learning

  • January 18, 2020
Table of Contents

Personalizing Spotify Home with Machine Learning

Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique.

Across our engineering community, we are working to unite autonomous teams and empower them to be more efficient by establishing best practices for tools and methods. Our recent adoption of a standardized machine learning infrastructure provides our engineers with the environment and tools that enable them to quickly create and iterate on models. We call it our ‘Paved Road’ approach, which includes utilizing services from TensorFlow, Kubeflow, and the Google Cloud Platform.

Before coming to Spotify, I worked on personalization algorithms and the home screen at Netflix. My previous experience is quite similar to the work I’m doing now at Spotify as the Vice President of Engineering and Head of Machine Learning. However, personalizing Spotify’s Home screen presented a new set of challenges, which I’ll share later.

Source: spotify.com

Tags :
Share :
comments powered by Disqus

Related Posts

Introducing LCA: Loss Change Allocation for Neural Network Training

Introducing LCA: Loss Change Allocation for Neural Network Training

Neural networks (NNs) have become prolific over the last decade and now power machine learning across the industry. At Uber, we use NNs for a variety of purposes, including detecting and predicting object motion for self-driving vehicles, responding more quickly to customers, and building better maps. While many NNs perform quite well at their tasks, networks are fundamentally complex systems, and their training and operation is still poorly understood.

Read More
The Effects of Mixing Machine Learning and Human Judgment

The Effects of Mixing Machine Learning and Human Judgment

In 1997 IBM’s Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from Jeopardy to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2

Read More