Facebook AI Feed Algorithm: 7 Ways to Customize Your Feed

Social media platforms have traditionally relied on recommendation algorithms to decide which posts, videos and other content appear in a user’s feed. These systems learn from behavior such as likes, comments, shares and viewing activity, gradually building an understanding of what each person may want to see.

However, recommendation algorithms do not always get it right. Users can find themselves seeing too many videos about a topic they watched only once, repeated recommendations that no longer match their interests or content that feels increasingly disconnected from their preferences.

Meta is addressing this challenge with a Facebook feature called Your Algorithm, which allows users to communicate their interests directly to the platform using natural language. Announced on October 13, 2026, the feature introduces AI-powered controls for customizing recommendations in Facebook Feed and Reels.

Instead of depending entirely on the platform to infer preferences from behavior, users can tell Facebook which topics they want to see more often or less frequently. They can access customization controls through the three-dot menu on posts and use options such as “Interested” and “Not interested.” Facebook may also ask follow-up questions to understand their preferences in more detail.

According to Meta’s official announcement, the recommendation system uses large language models (LLMs) to interpret the interests, context and intent expressed through natural language. The company says recommendations can adapt in real time, including within the same session.

The development builds on Meta’s earlier personalization experiments on Instagram and Threads. With Facebook adopting similar capabilities, the company is moving toward a more interactive relationship between users and the AI systems that determine what they see.

The change could give users more direct control over content discovery, but it does not mean that Facebook’s entire recommendation system is being replaced with a manually controlled feed. The platform will continue using behavioral signals and other ranking mechanisms alongside the new preferences.

What Is the Facebook AI Feed Algorithm?
How Does the Your Algorithm Feature Work?
Key Features of Facebook’s AI-Powered Feed Customization
Facebook’s Algorithm Before and After AI Customization
Benefits for Users, Creators and Businesses
Limitations and Privacy Considerations
How to Customize Your Facebook Feed
The Future of AI-Powered Social Media Recommendations
Frequently Asked Questions
Conclusion

What Is the Facebook AI Feed Algorithm?

The Facebook AI Feed Algorithm refers to the recommendation and ranking systems that determine which content users see in Feed and Reels. These systems consider multiple signals to estimate which posts are likely to be useful, interesting or relevant to a particular person.

Facebook has long used machine learning to rank content based on interactions and other signals. For example, a person who regularly watches travel videos, interacts with technology posts and follows cooking pages may receive recommendations related to those interests.

The difficulty is that behavior does not always communicate intent clearly. Watching a video does not necessarily mean someone wants more videos like it. A user might watch a clip out of curiosity, because a friend shared it or simply because it appeared automatically while scrolling.

Similarly, a person’s interests change over time. Someone who spends several weeks learning about artificial intelligence may later want to return to sports, entertainment or family-related content. An algorithm that relies heavily on historical behavior can take time to reflect that change.

Meta’s new Your Algorithm feature is designed to make this process more direct. Users can manage the topics they want to see in Feed and Reels and explain their preferences in their own words.

The feature is not a completely new recommendation engine operating independently of Facebook’s existing systems. Instead, it introduces an additional way to influence how those systems rank and recommend content.

Why Is Meta Changing Feed Personalization?

Recommendation algorithms can make large platforms easier to navigate by filtering enormous amounts of content into a more manageable selection. However, a system that relies primarily on inferred preferences can sometimes reinforce unwanted patterns.

For example, a user interested in cybersecurity might receive an increasing number of sensational posts about cyberattacks, even when they prefer practical security advice and technical analysis.

Another user might enjoy occasional cooking videos but not want their entire Reels feed filled with recipes. Someone interested in artificial intelligence may want research updates and developer tools rather than generic AI-generated entertainment.

A natural-language interface provides a more specific way to communicate those distinctions.

A user could express a preference for “technical AI news, cybersecurity research and software development tutorials, with fewer celebrity posts and viral reaction videos.” The system can use this input alongside existing recommendation signals to adjust its ranking.

The benefit is not simply the ability to select broad categories. It is the opportunity to explain what a topic means to the individual user and what kind of content they prefer within that topic.

What Does Your Algorithm Actually Control?

The feature is designed to influence recommendations in Facebook Feed and Reels. It allows users to add and manage interests and give direct feedback about posts.

However, it should not be interpreted as a control panel for every aspect of Facebook. It does not necessarily let users dictate every post that appears, eliminate all unwanted content or independently configure every system involved in ads, notifications and other parts of the service.

Facebook continues to rank content using a broad range of signals, including existing engagement patterns. Your Algorithm adds another layer of user input rather than replacing the underlying recommendation system altogether.

How Does Your Algorithm Work?

How Does the Your Algorithm Feature Work?

Meta describes Your Algorithm as a new way for users to shape the recommendations they receive by communicating their interests directly. The feature combines natural-language input with existing ranking signals to make recommendations more responsive to individual preferences.

1. Open the Customization Controls

Users can access Your Algorithm through the three-dot menu associated with content on Facebook.

This menu is becoming an important point of access for recommendation controls because users often know what they want to change while looking at a particular post or video.

For example, someone might encounter several posts about a topic they do not enjoy. Rather than leaving the feed and searching through settings, they can use the available feedback controls directly from the content.

The dedicated Your Algorithm area provides a place to add and manage preferred topics.

2. Choose What Interests You

Facebook lets users communicate whether they are interested or not interested in particular content. Depending on the interface and the selected option, the platform may ask for additional details about what the user liked or disliked.

This creates a more explicit feedback process than relying only on passive behavior.

Imagine a person who enjoys software development content but dislikes exaggerated headlines about AI replacing all programmers. Their feedback could help Facebook distinguish between the technical topic they like and the particular style of content they want to see less often.

The quality of the result will depend on how accurately the recommendation system interprets the preference and whether suitable content is available.

3. Describe Preferences in Natural Language

One of the feature’s key differences is the ability to express preferences in ordinary language.

Instead of selecting only broad topics, users can explain the kind of content they want to see within those subjects.

For example, a user could indicate that they prefer science-based fitness guidance over celebrity workout trends. Someone interested in finance might prefer educational explainers and market analysis instead of speculative posts or promotional content.

This type of input can communicate details that are difficult to capture through a simple like or dislike.

It does not guarantee that every recommendation will match the instruction perfectly, but it provides the system with a clearer signal of user intent.

4. AI Interprets the Meaning Behind Your Request

Meta says large language models help interpret the interest, context and intent expressed through natural language.

An LLM can process more nuanced instructions than a simple keyword filter. For example, the request “show me more in-depth cybersecurity tutorials, but fewer fear-driven stories about hacking” contains both a topic preference and a distinction about the type of content preferred.

Facebook’s system can combine this information with signals it already gathers from engagement and viewing activity.

That combination is important because a natural-language request cannot describe every possible post a user might encounter. The underlying recommendation system still needs to assess individual pieces of content and decide how they relate to the person’s preferences.

5. Recommendations Adapt in Real Time

Meta says the system can update recommendations in real time and adjust them within the same session.

This means a user does not necessarily have to wait for a lengthy period of additional engagement before the system reflects a newly expressed preference.

For example, someone who wants to focus on photography tutorials for a project could update their interests and potentially begin seeing more relevant photography content during the same session.

Real-time adaptation describes how the ranking system responds to new feedback. It is not a guarantee that every post will immediately match the user’s preferences or that all previous recommendation patterns will disappear.

6. Existing Behavioral Signals Still Matter

Your Algorithm adds explicit feedback, but Facebook’s existing recommendation systems continue to use signals such as likes, comments and video views, among many others.

This creates a hybrid personalization model. The platform can use direct statements about what someone wants to see while continuing to learn from how they interact with the content presented.

The approach could help resolve some mismatches between behavior and intent. If a user watches one video out of curiosity but explicitly says they are not interested in that subject, the direct feedback gives the system additional context.

The extent to which this changes recommendations over time will depend on how Facebook combines these signals.

Key Features of Facebook's AI-Powered Feed Customization

The new system introduces several changes to how Facebook users can influence the content recommended to them.

1. A Dedicated Your Algorithm Interface

The feature provides a dedicated space for users to add and manage interests relevant to Feed and Reels.

Having a separate interface for preferences can make it easier to review and adjust what the system understands about a user’s interests. It also creates a more direct way to make changes instead of waiting for the algorithm to infer them from repeated interactions.

The dedicated control panel is intended to complement the feedback tools available from individual posts.

2. Natural-Language Interest Descriptions

Users can communicate preferences in their own words rather than relying exclusively on fixed categories or individual interactions.

This makes it possible to provide more context about what they like or dislike. A user may be interested in technology news but prefer practical product analysis over speculative rumors. Another may enjoy travel content while wanting to avoid expensive luxury travel recommendations.

The advantage is flexibility. The challenge is that the platform must interpret the instruction accurately and translate it into relevant ranking decisions.

3. Interested and Not Interested Feedback

The updated controls let users provide direct feedback on the content appearing in Feed and Reels.

When a user selects Interested or Not interested, Facebook may request further information to understand the preference. This feedback can help distinguish between a broad topic and the type of material a person actually wants to consume.

It also creates an opportunity to correct recommendations that are trending in an unwanted direction.

4. Real-Time Recommendation Updates

Meta says recommendations respond to changes in user feedback in real time, including within the same session.

This could make the feed more adaptable when interests change temporarily or when a user wants to explore a new subject.

For instance, someone researching a home renovation project could ask for more practical design and building content, then update the preference later when they return to their regular interests.

Real-time adaptation can reduce the delay between expressing a preference and seeing its potential effect.

5. More Conversational Ranking Systems

The feature reflects Meta’s use of large language models in content ranking.

Traditional personalization relies heavily on interpreting behavioral data and ranking content according to estimated relevance. Adding natural-language input creates a more conversational layer in which users can directly express distinctions that might otherwise be difficult for the system to infer.

This does not necessarily mean Facebook is using a chatbot to individually assess every post. The announcement describes LLMs as part of the broader ranking process that interprets user preferences alongside existing signals.

6. Easier Access From Individual Posts

Meta says it has updated the three-dot menu on every post to make customization controls easier to access.

This is a small but practical interface change. Rather than requiring users to navigate several menus, they can respond to an unwanted recommendation while it is visible.

Direct feedback is particularly useful when the same kind of content appears repeatedly. Users can indicate a preference at the point where the problem occurs.

7. A More Personalized Reels Experience

Your Algorithm applies to Reels as well as Feed.

Reels is designed around short-form video discovery, and recommendation systems play a substantial role in deciding which clips users encounter. The new controls provide a way to steer the mix of topics and formats that appears there.

A person could express a preference for educational technology videos, cooking demonstrations or travel guides while requesting less content in another category.

This does not guarantee complete control over the Reels experience. Recommendations will still depend on available content and Facebook’s broader ranking mechanisms.

Facebook’s Algorithm Before and After AI Customization

The most important change is not that Facebook has stopped using recommendation algorithms. It is that the company is providing an additional way for people to influence the ranking process.

Aspect	Traditional personalization	Your Algorithm approach
Preference signals	Likes, comments, views and other engagement signals	Existing signals plus more direct natural-language feedback
User input	Often indirect or post-specific	Directly describe and manage topics of interest
Topic control	Preferences inferred from behavior and available controls	Dedicated area for adding and managing interests
Feedback interface	Existing content feedback tools	Updated three-dot menu and Interested/Not interested options
Adaptation	Depends on ranking systems and accumulated signals	Meta says changes can affect recommendations in real time
Scope	Multiple ranking systems across Facebook	New customization controls specifically for Feed and Reels

The new system does not remove personalization based on behavior. Meta says it combines freeform text with information it already derives from how users engage with content.

The central difference is the availability of more explicit user input.

Why This Matters for Content Discovery

Recommendation systems have to estimate relevance across huge amounts of material. They cannot ask users to manually rate every video, article or post.

That is why they use behavioral signals. The downside is that behavior can be ambiguous.

A person may watch a video because it is unusual, controversial or simply unavoidable while scrolling. The system could interpret that engagement as interest even when the person does not want similar material in the future.

By accepting more direct feedback, Facebook has another way to distinguish between content that attracted attention and content a user genuinely wants to see.

If the system interprets these instructions well, this could improve the quality of recommendations. The actual outcome will depend on the ranking model, the available content and how the user’s different signals are weighted.

Benefits for Users, Creators and Businesses

Greater Control Over the User Experience

The most immediate benefit is the ability to express preferences directly rather than depending exclusively on the algorithm to learn from behavior.

Users may find it easier to steer their feeds away from repetitive topics and toward information they actually value.

For people who use Facebook for multiple purposes—keeping in touch with friends, following news, learning new skills and discovering entertainment—this flexibility could help create a more balanced feed.

The feature also gives users a way to communicate temporary interests instead of assuming their preferences always remain the same.

Opportunities for Content Creators

Creators depend on recommendation systems to reach audiences beyond their existing followers.

If users can communicate more specific interests, the content they encounter could become more closely aligned with their preferences. This may benefit creators whose material serves clearly defined audiences, such as professional software tutorials, specialist photography or educational science videos.

However, personalized preferences may also change distribution patterns. A creator’s reach could become more dependent on whether the content matches the interests users explicitly communicate, in addition to other ranking signals.

Meta has not suggested that the new feature guarantees greater reach or higher engagement for creators. Its primary purpose is to give users more control over recommendations.

Better Content Discovery for Businesses

Businesses use Facebook to publish updates, educate customers, demonstrate products and reach relevant audiences.

More specific personalization could help users discover content that matches their interests, including product tutorials, industry updates and practical advice.

For example, a technology company could publish educational content about cybersecurity for business owners. Users who have expressed an interest in cybersecurity education may be more receptive to such material than users seeking unrelated entertainment.

Still, the feature is not an advertising guarantee or a direct targeting control for businesses. It affects user-side recommendations and should not be confused with Meta’s separate advertising tools.

More Flexible Interest Management

User preferences can change because of personal projects, professional responsibilities, seasonal interests or changing circumstances.

The ability to add and manage topics directly could make the feed more responsive to those changes.

A person preparing for an exam may temporarily want educational content, while someone planning a trip could prioritize travel recommendations. When their priorities change, they can update their preferences again.

This type of flexibility is useful because personalization becomes less dependent on the assumption that yesterday’s behavior accurately predicts today’s interests.

Limitations and Privacy Considerations

Although Facebook’s new AI customization tools may improve content discovery, several limitations deserve attention.

Personalization Does Not Equal Complete Control

The Your Algorithm feature lets users express preferences and influence recommendations, but it does not give them direct control over every aspect of Facebook’s ranking systems.

The platform will continue to combine multiple signals, and users may still encounter content that does not perfectly match their instructions.

The feature should be understood as a way to guide recommendations, not as a promise that the feed will contain only user-selected topics.

The AI May Misinterpret Preferences

Natural-language input can communicate more detail than a simple like or dislike, but interpreting it accurately is still a technical challenge.

Consider the instruction “show me more AI news, but avoid low-quality AI-generated videos.” The system must distinguish between the topic the user likes and the specific type of material they want to avoid.

If its interpretation is incomplete, recommendations may not improve as expected.

The same issue applies when users express nuanced or conflicting preferences. A person may want more technology news while also wanting less repetitive reporting about a single company.

Existing Behavioral Signals Continue to Matter

Meta’s announcement makes clear that its ranking systems still use information derived from engagement, including likes, comments and what users watch.

Consequently, direct preference statements do not necessarily override every other signal. If user behavior appears to conflict with a stated preference, the ranking system must decide how the information should be combined.

This means that people should not assume a single instruction will completely eliminate a particular category or guarantee that every future recommendation follows the request.

Privacy and Data Use Require Attention

Personalized recommendations depend on information about interests and behavior. Natural-language feedback adds more explicit information about what users want or do not want to see.

Users should understand that these controls are designed to improve content recommendations; they are not equivalent to disabling data collection or turning off every form of personalization.

Meta’s announcement describes the new ranking functionality but does not suggest that using Your Algorithm makes a Facebook account anonymous or prevents all data processing.

For details about information collected and how it is used, users should consult Meta’s Data Policy.

Content Quality Is a Separate Issue

A more relevant feed does not necessarily mean a more accurate or balanced one.

A user may ask for additional content about a favorite subject and receive posts that match the topic but contain misleading claims, exaggerated headlines or unsupported conclusions.

Recommendation systems determine what content is likely to be relevant, not whether every claim is true. Users should continue to assess sources, verify important information and avoid treating personalization as a guarantee of quality.

Rollout and Availability May Vary

Meta announced Your Algorithm on October 13, 2026, as a feature being rolled out on Facebook. The exact availability may vary as the company makes the update available to accounts and application versions.

If the option is not visible, it may not yet be available on that account. Users should check for application updates and look for the feature in the three-dot menu rather than assuming it has been universally enabled.

How to Customize Your Facebook Feed

The new controls are designed to make it easier to influence recommendations without having to manage every preference through a long settings process.

The following steps reflect Meta’s announced interface; names and placement may vary slightly as the feature rolls out.

Step 1: Open Facebook

Open the Facebook mobile app or the supported version of Facebook where the feature is available. Make sure the application is up to date.

The new interface may not appear on every account immediately because availability depends on the rollout.

Step 2: Find the Three-Dot Menu

When viewing a post or Reel, open its three-dot menu.

Meta has updated this menu to make the recommendation controls easier to reach. Look for the relevant interest or recommendation options.

Step 3: Select Your Feedback

Choose Interested or Not interested when those options are available.

If Facebook asks a follow-up question, provide additional detail about what you liked or disliked. This may help the recommendation system understand the distinction between a broad topic and the specific content you prefer.

Step 4: Open Your Algorithm

Find the dedicated Your Algorithm option to manage topics more directly.

Use the interface to add or adjust the interests you want represented in Feed and Reels. You may wish to specify both the subjects you enjoy and categories you want to see less frequently.

For example, a user could prioritize cybersecurity education, software development tutorials and practical AI research while reducing repetitive celebrity news.

Step 5: Review Your Recommendations

Continue using Facebook and assess whether the recommendations reflect your preferences.

Meta says the ranking systems can adapt in real time, but the extent of the change may vary. If the feed still contains unwanted material, use the available feedback controls to give more context.

The feature is intended to make personalization more interactive, so refining preferences over time may be more useful than expecting one instruction to produce a perfect feed immediately.

For the official explanation of the feature, visit Meta’s Your Algorithm announcement.

The Future of AI-Powered Social Media Recommendations

Meta’s Your Algorithm feature reflects a broader shift in how social media platforms approach personalization. Rather than leaving users to communicate their preferences indirectly through likes, comments and watch time, platforms are experimenting with ways for people to describe what they want to see.

Meta has already tested similar ideas on other services. In its Instagram experience, Your Algorithm provides controls for shaping recommendations, while Threads introduced Dear Algo, a feature that lets users express preferences about their feed.

The Verge’s report on Instagram’s Your Algorithm feature and its coverage of Threads’ Dear Algo provide context for this direction. Facebook’s rollout extends the concept into another major Meta platform.

From Passive Recommendations to Interactive Personalization

Historically, users have largely adapted to the content recommended by a platform. They can follow or unfollow accounts and provide feedback, but the ranking system itself remains relatively opaque to many people.

Natural-language controls could make personalization easier to understand and use.

A user may not know how to adjust an algorithm through technical settings, but can explain that they want more educational videos, fewer sensational headlines or a wider range of perspectives on a subject.

The platform must then translate those preferences into ranking decisions. This creates a more interactive form of personalization while preserving the automated systems needed to process enormous amounts of content.

Large Language Models as an Interface to Ranking Systems

Large language models are especially relevant because they can interpret ordinary language, distinguish between related concepts and process instructions that include multiple conditions.

Meta says its ranking systems use LLMs to incorporate the interests, context and intent expressed through freeform text.

That does not mean an LLM alone determines every recommendation. Meta’s announcement describes language models as part of an evolving ranking process that also uses existing signals.

The important change is that people can express richer preferences in a form the system can interpret, rather than being limited to a small set of predefined topic labels.

A More Competitive Social Media Experience

Personalization is a significant part of the user experience on modern social media platforms. The ability to steer recommendations may become a useful differentiator as companies compete for attention and retention.

Users who feel that their feeds reflect their interests more accurately may find a platform more helpful. However, meaningful control also requires transparency about what the controls do, what they cannot change and how user preferences interact with other ranking signals.

The quality of the implementation will matter as much as the introduction of natural-language input.

The Challenge of Balancing Relevance and Diversity

One long-term question is whether increasingly precise personalization will expose users to a narrower range of subjects and viewpoints.

A user who asks for more of a favorite topic may benefit from relevant information, but an overly narrow recommendation pattern could reduce the chance of discovering unfamiliar subjects.

A well-designed system needs to balance user preferences with useful discovery. People should be able to guide their feed without necessarily losing the opportunity to encounter new ideas and perspectives.

Facebook’s new controls give users another way to steer their recommendations. Whether this results in a more useful overall experience will depend on how the ranking systems balance explicit preferences, existing behavior and the range of content available.

Frequently Asked Questions

What is Facebook’s Your Algorithm feature?

Your Algorithm is a Facebook feature that lets users manage their interests and influence recommendations in Feed and Reels. It introduces more direct feedback options, including natural-language descriptions of what users want to see more or less often.

How does the Facebook AI Feed Algorithm work?

Facebook uses multiple signals, including likes, comments, video views and other engagement data, to rank content. The new feature adds more explicit interest preferences, which Meta says are interpreted with help from large language models and combined with existing signals.

Can I tell Facebook exactly what I want to see?

You can communicate preferences through the available customization tools and describe your interests in your own words. However, Facebook continues to use its broader ranking systems, so this does not guarantee that every recommendation will match your instructions.

How do I access Your Algorithm?

Meta says users can find the feature through the three-dot menu on Facebook posts. It also provides Interested and Not interested options to help users give feedback and manage their recommendations. Availability may vary during rollout.

Does Your Algorithm work with Facebook Reels?

Yes. Meta announced that the feature applies to both Feed and Reels, allowing users to influence recommendations across these two content experiences.

Does Facebook use AI to personalize recommendations?

Yes. Facebook already uses AI-driven ranking systems, and Meta says large language models are now helping its systems interpret the interests, context and intent behind natural-language preferences.

Will this feature stop Facebook from showing unwanted content?

Not entirely. Your Algorithm is designed to influence recommendations, but it does not guarantee that all unwanted content will disappear. The platform continues to combine user feedback with other signals and available content.

Does customizing my feed disable Facebook’s data collection?

No. The feature is intended to influence content recommendations, not disable Facebook’s data processing or all forms of personalization. Users should consult Meta’s Data Policy for information about how data is collected and used.

Is Your Algorithm available to all Facebook users?

Meta announced a rollout on October 13, 2026. The exact availability may vary by account, region and application version. Check Facebook’s post menus and the official announcement for the latest information.

Conclusion

Facebook’s Your Algorithm feature gives users a more direct way to shape the content recommended in Feed and Reels. By accepting natural-language preferences and using large language models to interpret them, Meta aims to make recommendations more responsive to what people actually want to see.

The change is significant because it adds explicit instructions to a personalization system that has traditionally relied heavily on inferred behavior. Users can communicate more nuanced preferences, adjust topics and provide feedback directly from individual posts.

However, the feature does not replace Facebook’s recommendation algorithms or give users complete control over everything they encounter. Existing engagement signals and broader ranking systems remain important, and recommendation quality will depend on how effectively those signals are combined.

If implemented well, Your Algorithm could make social media personalization more transparent and useful. Its long-term success will depend on whether users can reliably steer their experience without sacrificing content diversity, privacy or the ability to discover something new.

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