A recommendation engine playground that should hopefully make playing with music recommendations easy.
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.
ListenBrainz
Track, explore, visualise and share the music you listen to. Follow your favourites and discover great new music.
Bluesky "For You" feed playground
This page shows recommendations generated by the For You custom feed. The algorithm has three simple steps:
Bluesky "For You" feed playground
This page shows recommendations generated by the For You custom feed. The algorithm has three simple steps:
2002: Last.fm and Audioscrobbler Herald the Social Web
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.
Rocksky (@rocksky.app)
A decentralized music tracking and discovery platform built on @atproto.com 🎵 . Scrobble your plays, share playlists, and explore listening trends, not affiliated with @bsky.app Support the project → https://github.com/sponsors/tsirysndr
Safety and Privacy center
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.
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.
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.
Two tower models for retrieval of recommendations
Fourth post in this series on personalized recommendations
Mega Viral Games
Discover amazing games from across the internet. Like games to get personalized recommendations.
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.
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
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.”
Recommender Systems
Letting Users Choose Recommender Algorithms: An Experimental Study
Agentic Taste Modeling | lab notes #8
A Bluesky feed for one · Adam Wiggins
How the Substack feed is learning to understand your reading journey
