At Spotify, we aim to create great and unique experiences for each user. Our goal is to connect everyone with what they love and help them discover something new. No two listeners are the same, so everyone's Spotify experience, and many of our recommendations, are personalized. When asked what they like about Spotify, most listeners cite our personalization as their top feature. You might wonder how we generate these recommendations across the Home feed, playlists, search results or other parts of the service, and we want to help demystify how they work.
Introducing Verified by Spotify, a Signal of Authenticity and Trust for the Artists Behind the Music — Spotify
Music is evolving, and so are the ways you discover and connect with artists. In the AI era, it’s more important than ever to be able to trust the authenticity of the music you listen to. At Spotify, our focus is providing you with more context about artists and their music, so you can build...
Building a More Trusted Podcast Experience for Creators and Listeners — Spotify
Podcasting continues to evolve, and so does Spotify. As we build what comes next, one thing remains constant: This is a medium built on connection. It lives in the chemistry between co-hosts, in the relationship between creators and the fans who return week after week, and in the dialogue around ideas and culture. As creation...
How Spotify’s Discover Weekly cracked human curation at internet scale
The Verge is about technology and how it makes us feel. Founded in 2011, we offer our audience everything from breaking news to reviews to award-winning features and investigations, on our site, in video, and in podcasts.
Michael Tsai - Blog - App Store Personalized Recommendations and Keylogging
This week, Apple announced a series of discovery features that will personalize app recommendations based on users’ interests and behavior, providing a new way for developers to have their app discovered.
Pixie: A System for Recommending 3+ Billion Items to200+ Million Users in Real-Time
User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a …
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large
Collective Social | Track what you love. Share what matters.
Curate lists, track your progress, and share recommendations across books, movies, TV shows, and more — all on the open social web.
I paid for Spotify playlist placements so you don’t have to
As part of my ongoing mission to throw money into thin air I decided to promote some of Corserine’s songs on Spotify. Objectives: increase monthly streams on my Spotify artist page so I can g…
SlopTracker — Exposing AI-Generated Artists on Spotify
AI-generated artists are flooding Spotify, racking up millions of streams, and siphoning revenue from real musicians. See the receipts.
metabrainz/troi-recommendation-playground
A recommendation engine playground that should hopefully make playing with music recommendations easy.
Personalization at Bluesky
The past, present, and future of personalization of the Discover feed
Personalization at Bluesky
The past, present, and future of personalization of the Discover feed
Letting Users Choose Recommender Algorithms: An Experimental Study
Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.
How Platform Recommenders Work – Center for Human-Compatible Artificial Intelligence
A recommender system (or simply ‘recommender’) is an algorithm that takes a large set of items and determines which of those to display to a user—think the Facebook News Feed, the Twitter timeline, Google News, or the YouTube homepage. Recommenders are necessary tools to help navigate the sheer volume of content produced each day, but their scale and rapid development can cause unintended consequences. Facebook’s algorithms have been blamed for radicalizing users, TikTok’s for inundating teens with eating-disorder videos, and Twitter’s for political bias.
Cool new feature from the @graze.social team: add personalization to your feeds. Lots of details about their approach to "composable personalization" in this post graze.leaflet.pub/3mgascfb6uc2e
For You, For Everyone - Graze Newsletter
graze.leaflet.pubI'm building an alternative streaming option for artists called tracklist:// where they will be able to self-host their music catalog and stream it directly to fans. As musicians ourselves, our goal is to empower artists with connection. Sign up for our waitlist here; tracklist.diy
tracklist :// — music
www.tracklist.diy
Hearing Things
“It can be a little scary,” Hotline TNT frontman Will Anderson says of taking his music off Spotify, “but one of the things we’re trying to do is illustrate that it might not be as scary as you think.”
