Facebook Gives Users More Control Over Their Feed With New AI-Powered Algorithm

Meta is introducing a new AI-powered feature that allows Facebook users to have greater control over the content recommended to them in their Feed and Reels. The feature, called Your Algorithm, gives users a more direct way to tell Facebook what they want to see more or less of.

Announced on October 13, 2026, the update reflects Meta’s broader effort to make social media recommendations more personalized and responsive to individual interests.

Facebook Users Can Now Customize Their Recommendations

For years, Facebook has relied on recommendation algorithms to determine which posts, videos, and other content appear in users’ feeds. These systems analyze various signals, including likes, comments, viewing activity, and other interactions, to predict what people may find interesting.

With Your Algorithm, Facebook is giving users another way to influence those recommendations. Instead of relying entirely on the platform to interpret their behavior, users can describe their interests in their own words.

For example, someone who wants to see more technology news, AI developments, music updates, or football content can communicate those preferences to help shape their recommendations.

Meta says changes can influence recommendations in real time, allowing users to adjust their experience as their interests change.

How the New Feature Works

Facebook users can access Your Algorithm through the three-dot menu on posts. The platform is also expanding feedback options that allow people to indicate whether they are interested or not interested in particular content.

After users select one of these options, Facebook may ask follow-up questions to better understand what they liked or disliked. Users can then provide more detailed feedback using natural language.

The goal is to make content recommendations more interactive, giving people a more straightforward way to influence what appears in their feeds.

Artificial Intelligence Powers the New Experience

The feature uses large language models, or LLMs, to help Facebook interpret users’ interests, context, and intentions expressed through everyday language.

This approach could make recommendations more flexible than systems that depend primarily on interpreting clicks, likes, watch time, and other behavioral signals.

However, users should not assume that their preferences will completely determine everything they see. Facebook’s recommendation systems continue to use a range of signals to rank and recommend content.

Following Instagram and Threads

Facebook’s update builds on similar personalization experiments across Meta’s other platforms.

Instagram has introduced its own Your Algorithm feature, allowing users to influence recommendations, while Threads offers a related tool called Dear Algo.

By expanding these capabilities across its social platforms, Meta is moving toward a more interactive model of content discovery, where users can communicate their preferences instead of relying solely on automated recommendations.

What This Means for Facebook Users

The update could help users discover more relevant content and reduce recommendations that do not match their interests. It may also give people a clearer way to communicate their preferences without repeatedly engaging with content they do not enjoy.

For creators, publishers, and businesses, however, changes to recommendation systems could affect how their content reaches audiences. As users gain more influence over their feeds, producing relevant, engaging material may become even more important.

The feature also raises broader questions about how much control users should have over algorithmic recommendations and how clearly social media companies explain the systems shaping their online experiences.

Conclusion

Facebook’s Your Algorithm feature represents another step toward more personalized social media recommendations. By allowing users to describe their interests and adjust preferences through AI-powered tools, Meta is making its content-ranking systems more interactive.

The key question will be whether these new controls give users meaningful influence over their feeds while maintaining a useful balance between personal preferences and content discovery.