Community-based embeddings for users, tweets, and content recommendation
Deep Dive: Inside X (fka Twitter)’s Recommendation Algorithm
Decoding the Machine That Powers Your “For You” Timeline ,When Elon Musk open-sourced Twitter’s recommendation algorithm in March 2023, it…
X Open-Sourced Its Algorithm on GitHub: Here's What the Code Actually Says
xAI published the full X recommendation algorithm on GitHub in January 2026 and updated it again in May. We read the code so you don't have to. Here is what the source actually reveals about how your posts get ranked — and the exact tactics to use it.
metabrainz/troi-recommendation-playground
A recommendation engine playground that should hopefully make playing with music recommendations easy.
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.
Understanding Social Media Recommendation Algorithms
Access the PDF version of this essay by clicking the icon to the right.
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.
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.
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.
Elon Musk on Twitter / X
Interesting. Our algorithm tries to optimize time spent on X, so links don’t get as much attention, because there is less time spent if people click away. Best thing is to post content in long form on this platform.— Elon Musk (@elonmusk) October 3, 2023
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 …
BridgEU: new project to study how recommendation systems can reduce polarization | SynoSys
A new collaborative project with Philipp Lorenz-Spreen, Eurosky, and SPRIND explores whether social media recommendation algorithms can strengthen public discourse instead of reinforcing divisions.
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
readme.club - Community-driven resource sharing for Xteink users
Share and discover language files, documentation, tools, wallpapers, feature requests, and more
A few weeks of X’s algorithm can make you more right-wing – and it doesn’t wear off quickly
Elon Musk’s social media platform is boosting conservative content – and it’s having long-lasting effects on how users see the world.
