It’s time we talk about how recommendation algorithms work

These systems are complex

Having worked on recommendation algorithms and conducted research into how top-performing systems operate, I’ve spent time understanding what drives their performance and how they shape the information environments we live in.

This article is about how these systems work, why they behave the way they do, and what role we play inside them.

TL;DR: We are not just passive users. We are participants in how these systems evolve.

How the system learns about you

When you first join a platform, the algorithm gathers everything it can. Basic attributes such as location, profession, or language may be used to place you into initial groups.

But the most important signals come from behavior. The system observes what you engage with: what you search, what you read, what you save, what you share, and what you scroll past.

At the same time, every piece of content also carries attributes: topic, format, tone, and information about the creator.

Over time, the algorithm learns patterns. If it notices that you consistently engage with certain creators, viewpoints, or topics, it begins to prioritize similar content. This is how hyper-personalized feeds emerge.

Each of us begins to inhabit a slightly different informational environment, one shaped by our own past behavior.

Why platforms design systems this way

Recommendation systems are typically optimized for one central metric: attention. The longer people stay on a platform, the more opportunities there are for engagement and revenue.

To achieve this, algorithms group users with similar patterns of behavior. If a cluster of users spends more time interacting with certain posts, those posts are shown to other users with similar profiles. That’s how content spreads.

What we often call virality is frequently the result of these clustering dynamics. From an engineering perspective, these systems are fascinating to build. But from a societal perspective, they introduce new challenges.

The unintended consequences

When attention becomes the primary objective, several patterns can emerge.

• People spend increasing amounts of time consuming information rather than engaging with the world around them.
• New accounts can sometimes be exposed to extreme or emotionally charged content early in their lifecycle , which can shape perceptions, particularly among younger users.
• Creators may begin optimizing their work for algorithmic visibility rather than for meaningful human communication.
• Over time, the system starts influencing not only what we see, but also what we create.

The part we often forget

Algorithms shape our feeds. But we shape the brain of algorithms too. Every interaction sends a signal. What we click, what we ignore, what we linger on, all of these actions contribute to how the system learns about us.

In other words, recommendation systems are constantly adapting to the collective behavior of their users. Which means we have more agency than we often assume.

How to take back some control

Instead of approaching platforms passively, it helps to think of them as systems that can be trained.

Start with simple questions: Why are you here? What do you want this platform to give you? Knowledge? Inspiration? Professional insight? Once you decide that, begin interacting more intentionally. Scroll past content that doesn’t align with your goals. Save, share, and engage with material that does.

Within days, most recommendation systems will begin adjusting. Your feed starts to change. Instead of a distraction engine, it can become something closer to a personal learning environment.

Curate, don’t just consume

The systems around us are powerful, but they are not fixed. They are shaped by design choices and by human behavior. The more consciously we interact with them, the more influence we have over the environments they create.

Curate your attention.

Share this post with your reflections to open the conversation wider.

And if you are responsible for younger users, help them understand that feeds are not neutral streams of information. Feeds are systems that respond to behavior. Learning how to guide them is becoming an essential skill in the digital age.