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.
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…
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.
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:
Understanding Social Media Recommendation Algorithms
Access the PDF version of this essay by clicking the icon to the right.
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.
Paper Skygest: Personalized Academic Recommendations on Bluesky
We build, deploy, and evaluate Paper Skygest, a custom personalized social feed for scientific content posted by a user's network on Bluesky and the AT Protocol. We leverage a new capability on emerging decentralized social media platforms: the ability for anyone to build and deploy feeds for other users, to use just as they would a native platform-built feed. To our knowledge, Paper Skygest is the first and largest such continuously deployed personalized social media feed by academics, with over 50,000 weekly uses by over 1,000 daily active users, all organically acquired. First, we quantitatively and qualitatively evaluate Paper Skygest usage, showing that it has sustained usage and satisfies users; we further show adoption of Paper Skygest increases a user's interactions with posts about research, and how interaction rates change as a function of post order. Second, we share our full code and describe our system architecture, to support other academics in building and deploying such feeds sustainably. Third, we overview the potential of custom feeds such as Paper Skygest for studying algorithm designs, building for user agency, and running recommender system experiments with organic users without partnering with a centralized platform.
How the Twitter/X Algorithm Works in 2026 (Source Code)
Twitter published its ranking weights on GitHub. A reply is worth 150x a like. Free accounts get 10x less reach. Full scoring system explained.
How Twitter’s Algorithm Works: A High-level Breakdown
On March 31st, 2023, Twitter made an unprecedented move by releasing the source code for their algorithm. The move is unprecedented because…
metabrainz/troi-recommendation-playground
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.
Social Media Feed Ranking Algorithms: Guide to Field Experiments
IC2S2'26 Tutorial | Social Media Feed Ranking Algorithms: Guide to Field Experiments
WTF: the who to follow service at Twitter | Request PDF
Request PDF | WTF: the who to follow service at Twitter | WTF ("Who to Follow") is Twitter's user recommendation service, which is responsible for creating millions of connections daily between users... | Find, read and cite all the research you need on ResearchGate
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 …
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
Wonderful talk by @sjgreenwood.bsky.social about @paper-feed.bsky.social! Custom feeds on Bluesky are unique resources for researchers interested in recommendation algorithms #IC2S2 Featuring many Bluesky friends like @graze.social @devingaffney.com @kissane.myatproto.social @aendra.com
