Meta announced the rollout of a new AI-powered feature, "Your Algorithm," for Facebook users. The tool allows people to describe their content preferences in natural language and thereby influence what appears in their Feed and Reels.
What the feature does
With "Your Algorithm," users can state topics or types of content they want to see more or less often instead of relying solely on automated recommendations. Meta says changes to preferences are applied in real time, making the platform's suggestions more relevant to individual users.
Facebook's three-dot menu has also been updated to make it easier to manage preferences from individual posts. Opening the menu lets users access the "Your Algorithm" option or choose simple markers such as "Interested" and "Not interested" to indicate whether a particular post aligns with their tastes.
Follow-up feedback and refinement
Meta says the platform may ask follow-up questions to clarify what a user liked or disliked, enabling more detailed feedback. Those responses are used to further refine the recommendation model for that user.
Strategic context
The introduction of "Your Algorithm" is part of Meta's broader effort to give people more control over their social media experience as AI plays a larger role in content discovery. The move marks a shift from fully automated recommendation systems toward interfaces that allow direct user input, similar to personalization features on streaming services.
The Facebook launch builds on Meta's earlier personalization work: Instagram introduced "Your Algorithm" in June, after first bringing the feature to Reels in December 2025. Threads has also rolled out a related capability called "Dear Algo," which similarly enables users to influence recommended content.
Why this matters
Allowing natural-language preference input and real-time adjustments could change how users encounter news, opinions, and entertainment on Facebook. While the feature may help surface more relevant content for individual users, its broader effects on information ecosystems and content exposure patterns will require ongoing observation.
