Following in Amazon's footsteps, two student projects independently use 'collaborative filtering' to bring recommendations and social networking to online music; soon they will join forces.
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.
Last.fm | Play music, find songs, and discover artists
The world's largest online music service. Listen online, find out more about your favourite artists, and get music recommendations, only at Last.fm
Last.fm | Play music, find songs, and discover artists
The world's largest online music service. Listen online, find out more about your favourite artists, and get music recommendations, only at Last.fm
metabrainz/troi-recommendation-playground
A recommendation engine playground that should hopefully make playing with music recommendations easy.
Ecosystem or Echo-System? Exploring Content Sharing across Alternative Media Domains
Kate Starbird,Ahmer Arif,Tom Wilson,Katherine Van Koevering,Katya Yefimova,Daniel Scarnecchia
Paper Notes - WTF - The Who to Follow Service at Twitter
Explore Twitter's recommendation system - in-memory graph processing with Cassovary, random walks, and SALSA for user suggestions.
The Music Discovery Problem – The Frugal Blog
I’ve been chatting with a good number of folks who are turning down streaming services lately for various reasons. I’ve blogged about this before, and if you’re looking for advice on how to make the switch, I and a lot of others have said things about it in the past.
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.
Smart Links for Music Marketing, Podcasts and Artist Promotion | Linkfire
Smart links for music marketing and podcast promotion. From your first single to your first world tour, Linkfire simplifies music and podcast promotion every step of the way. Trusted by major labels and over 50k artists.
Medium acquires Superfeedr
Publishing on the web is changing drastically. Medium is one of these few companies which are building the future of publishing and reading. We're excited to join forces with them and continue building the open web in this new era!
Collaborative filtering
Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.
I'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.”
Today some of the most innovative creators & podcasters are launching social websites that will bring millions of their fans to the social web: verge.surf.social decoder.surf.social versionhistory.surf.social vergecast.surf.social wired.surf.social 404media.surf.social poweruser.surf.social
This is definitely my feeling working with them on recommendation algorithm.
Mark Riedl
Fascinating experiment: current AI systems lack creativity to reliably pursue research arxiv.org/abs/2607.27191 - poor judgment about the bar for publishable research - uncreative responses in research design - ineffective backtracking from dead ends - poor resource awareness - instruction drift
