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

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
Building a document understanding pipeline with Google Cloud

Building a document understanding pipeline with Google Cloud

Document understanding is the practice of using AI and machine learning to extract data and insights from text and paper sources such as emails, PDFs, scanned documents, and more. In the past, capturing this unstructured or “dark data” has been an expensive, time-consuming, and error-prone process requiring manual data entry. Today, AI and machine learning have made great advances towards automating this process, enabling businesses to derive insights from and take advantage of this data that had been previously untapped.

Read More