AI Is Changing Social Media Algorithms: How Meta Uses LLMs to Personalize Recommendations

Social media algorithms are becoming more complex. Just a few years ago, discussions about Instagram and Facebook recommendations mainly revolved around likes, comments, saves, watch time, and other familiar signals. In 2026, this explanation is no longer enough.

Meta* is actively scaling artificial intelligence across its recommendation systems. The company uses hundreds and thousands of ML models and is developing sequence learning, foundation models, and LLM-scale recommendation models. Their goal is to identify the interests and intentions of each individual more accurately and select content that is more likely to be relevant to them.

For SMM, this is an important shift. The algorithm needs to do more than simply record a reaction to a post. Modern recommendation systems try to gain significantly more context about both the user and the content.

How Instagram and Facebook Recommendations Worked Before

Traditional recommendation algorithms were built around a large number of signals. The system analyzed user actions and used them to predict the likelihood of the next interaction.

Important signals include:

• post and video views;
• watch time;
• likes and comments;
• saves;
• shares;
• follows;
• previous interactions with the creator;
• history of interest in similar content.

These signals have not disappeared. It would be incorrect to assume that AI has completely replaced the familiar mechanics of social media platforms. Instead, something else is happening: the system is learning to analyze more data and identify more complex relationships between different signals.

The scale is clearly illustrated by Instagram's technical infrastructure. According to Meta Engineering, Instagram's recommendation system has grown to more than 1,000 ML models. They serve different products, objectives, and stages of the recommendation process.

What AI Is Changing in Meta's Algorithms

The main change is the shift from relatively isolated signals toward a deeper analysis of sequences of user actions and interests.

Consider a simple example. A person watches several Reels about marathon training, saves a post about running shoes, shows interest in an article about post-workout recovery, and begins interacting more frequently with running-related content.

For a simple system, these are separate events. For a more advanced AI model, they form a sequence that may indicate a persistent interest: the person runs or is preparing for a race.

Meta is developing precisely this approach. In its article about sequence learning in recommendation systems, the company's engineers explain how sequences of user experiences allow models to extract information that is difficult to obtain using only manually engineered features.

For marketers, the idea is quite simple: it is not only an individual like that matters. The overall context of user behavior matters as well.

Where LLMs Fit into Meta's Algorithms

It is important to avoid a common oversimplification here. It would be incorrect to say that Instagram has replaced its recommendation algorithm with a standard language model such as ChatGPT or Llama.

LLMs and recommendation models perform different tasks. A large language model primarily works with language and context. A recommendation model predicts a user's interest in specific objects: posts, Reels, ads, and other content.

However, the boundary between these areas is becoming more interesting. Meta is bringing scaling principles and architectural ideas developed through modern foundation models into its recommendation systems.

A good example is the Generative Ads Recommendation Model, or GEM. Meta describes GEM as a foundation model for ad recommendations built using an LLM-inspired approach and trained at the scale of large language models. The architecture is described in more detail in Meta's technical article about GEM.

In 2026, the company continued moving in this direction. Meta reported scaling Ads Recommender runtime models to LLM-scale complexity in order to model user interests and intentions more deeply.

Therefore, it is more accurate to talk about modern recommendation systems converging with certain principles and the scale of foundation models rather than saying that "Instagram now runs on LLMs."

AI Is Trying to Understand User Interests More Deeply

Personalization is gradually moving beyond the simple formula of "watched a video — show ten more similar ones."

Meta itself provided a good example. The company reported that users' interactions with Meta AI can be used as an additional signal for personalizing content and ads. If a user discusses hiking with AI, this may become a signal of interest in related posts, Reels, groups, or advertisements.

This means that data about user interests no longer comes only from traditional interactions with the feed.

In June 2026, Meta also announced further personalization changes: information about activity that businesses already share with Meta can be used not only for advertising but also to personalize Feed content and AI responses.

The result is a broader model of user interests. AI compares different signals and tries to predict which content will be relevant to a person at that particular moment.

What Meta's New Algorithms Change for SMM

For SMM specialists, the development of AI recommendations means that content should provide the algorithm with clear topical context. At the same time, engagement, retention, and reactions from real users remain important.

1. The Account's Topic Should Be Easy to Identify

If a profile regularly publishes content around a group of interconnected topics, it may be easier for the system to identify the appropriate audience for that content.

For example, a fitness blog can consistently cover workouts, nutrition, recovery, equipment, and race preparation. These are related entities within the same topical area.

A chaotic account that reviews a smartphone today, publishes a recipe tomorrow, and offers investment advice the day after provides far less obvious context.

2. Content Semantics Are Becoming More Important

SEO and SMM are gradually converging. This does not mean that you should insert dozens of search queries into a Reels description. The point is different: the topic of a post should be expressed clearly.

The headline, text, spoken words in the video, visuals, and description should support the overall meaning of the content. If a post is about promoting Reels, it is better to make this clear within the content itself rather than trying to communicate the topic exclusively through a set of hashtags.

3. Engagement Bait Is Becoming a Less Reliable Strategy

Calls to action such as "leave a + in the comments" can generate reactions, but they do not make a post useful on their own.

The better recommendation systems become at modeling genuine audience interests, the harder it becomes to build a long-term strategy solely around mechanically stimulating engagement.

A combination of signals is much more valuable: a person stops at a post, watches the video, saves it, sends it to a friend, visits the profile, or continues viewing content on the same topic.

4. Content Series Provide More Topical Context

Instead of publishing ten unrelated posts, it makes sense to test content clusters.

For example, a marketing blog could create a series covering:

• how the Instagram algorithm works;
• how Reels get into recommendations;
• which retention metrics to analyze;
• how to choose topics for short-form videos;
• why a video stops getting views.

Each post addresses a separate task, but all of them are connected by shared semantics.

5. Content Is Created Not Only for Followers

A recommendation model can find potentially interested audiences beyond the existing follower base. Therefore, the question "Will our followers like this?" should be complemented by another: "Will a new user be able to quickly understand the value of this post?"

This is especially important for Reels and other recommendation-driven content that is often shown to people who have no previous familiarity with the creator.

How to Optimize Content for AI Recommendations

There is no universal button for getting into recommendations. However, a content strategy can be structured so that both users and algorithms can more easily identify the topic and value of a post.

A practical sequence looks like this:

Topic → context → intent → retention → audience reaction → next related piece of content.

Suppose an online store sells travel products. Instead of a generic video titled "Our New Arrivals," it could create a video called "5 Things to Pack in Your Carry-On for a Long Flight." The user's intent becomes more specific. The next piece of content could focus on choosing a backpack, organizing luggage, or useful accessories for air travel.

As a result, a topical cluster is formed. The user receives a consistent stream of related content, while the system receives more connected signals.

When preparing content, it is useful to check five questions:

• Can the topic of the content be understood within a few seconds?
• Does the post match the overall topic of the account?
• Does it address a specific question or audience need?
• Is there a reason to watch it to the end, save it, or share it?
• Can the topic be continued in the next post?

Traditional Algorithms vs. AI Recommendations: What Is the Difference?

At the same time, it would be wrong to treat these two approaches as complete opposites. Meta's modern system does not discard older signals. It uses them within a more complex machine learning infrastructure.

What Will Change Next

The main trend is already clear: Meta is increasing the scale and complexity of the AI models used for personalization.

In 2026, the company reported further development of AI ranking for Facebook and Instagram. At the same time, new retrieval architectures and LLM-scale models for advertising are being developed.

For content creators, this means gradually moving away from attempts to find one "secret algorithm factor" and toward comprehensive work with audience interests.

Likes will remain a signal. Retention will remain a signal. Saves and shares will also continue to matter. But none of these exists in isolation; each becomes part of a much larger model of user behavior.

That is why a winning strategy looks fairly conservative: a clear topic, original content, clear intent, strong retention, and genuine reactions from the target audience.