metabrainz/troi-recommendation-playground

Reconstructing a music recommendation model

Marco Zocca

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

Twitter's For You Recommendation Algorithm

Twitter has open-sourced a majority of its recommendation algorithm. It offers an exciting opportunity for researchers, industry practitioners, and RecSys enthusiasts to take a close look at how Twitter computes the recommended feed for the For You page. This article described Twitter’s end-to-end recommender system along with relevant literature and code references.

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:

Rocksky - Apps on Google Play

Discover, track & share your music taste.

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.

SimClusters - X Recommendation Algorithm

Community-based embeddings for users, tweets, and content recommendation

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.

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.