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.
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…
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.
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.
SimClusters - X Recommendation Algorithm
Community-based embeddings for users, tweets, and content recommendation
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:
Bridging-Based Ranking
There is significant concern about the engagement-based ranking systems used by TikTok, Facebook, YouTube, etc. to recommend content. Bridging-based ranking systems can address one of the most dangerous aspects of such algorithmic recommendations—the push toward polarization and divisiveness that is tearing nations apart—and do so without reducing anonymity or increasing censorship. This report explores what bridging-based ranking is, how it helps (overcoming downsides of chronological feeds and middleware), addresses common objections, and provides early examples of its use and benefits in the wild. The report concludes by providing next steps for platforms, governments, funders, and researchers in order to accelerate the deployment of bridging.
Social Media Feed Ranking Algorithms: Guide to Field Experiments
IC2S2'26 Tutorial | Social Media Feed Ranking Algorithms: Guide to Field Experiments
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.
Understanding Social Media Recommendation Algorithms
Access the PDF version of this essay by clicking the icon to the right.
Xapian
Xapian is a free and open-source probabilistic information retrieval library, released under the GNU General Public License (GPL).[2] It is a full-text search engine library for programmers.
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 …
Personalized PageRank Estimation and Search: A Bidirectional Approach
We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a...
Personalized PageRank Estimation and Search: A Bidirectional Approach
We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution 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
I read the new EU Court ruling on algorithms and social platforms so you don't have to. Turns out to be the most consequential thing a European court has said about recommendation algorithms, and also it just breaks when you apply it to the atmosphere connectedplaces.online/the-algorithm-singular/
The Algorithm, Singular
connectedplaces.online
