Every scroll, swipe and stream is shaped by algorithms that learn what we like, but do you understand how they work?

How Recommendation Engines Shape What We See

Every time you open Spotify and find a new artist you love, or when Netflix suggests a series that keeps you hooked for days, or when TikTok seems to know what you’re interested in before you do, a recommendation engine is at work. These systems are not just helpful features, they are fundamental to how these platforms keep you engaged. They decide what you see, what you don’t and how long you spend online. But how do they actually work?

Most people experience recommendation engines as a black box. You click a song, video, or product and the app responds with more suggestions. It feels intuitive, even natural. But behind the scenes, there’s a careful mix of data science, machine learning and behavioural psychology shaping what appears on your screen. This blog will explore how these systems function, why they are designed the way they are and what that means for users and creators alike.

What Is a Recommendation Engine?

At its core, a recommendation engine is a system designed to predict what a user will want next. It takes the information it knows about you, compares it to patterns from millions of other users, and suggests content it believes you’ll enjoy. These suggestions can be based on your previous choices, people with similar tastes, or the behaviour of everyone using the platform.

For example, if you’ve watched several romantic comedies on Netflix, the system will likely prioritise that genre in future suggestions. But it goes much deeper than that. It might analyse what time of day you watch, whether you binge episodes or stop after one and how quickly you return to a series after pausing. This behavioural data becomes the input that feeds increasingly complex algorithms.

How Do They Work?

There are several techniques that recommendation engines use and most major platforms combine more than one to improve accuracy.

Collaborative Filtering is one of the most common approaches. This method makes suggestions based on the preferences of users who are similar to you. If person A and person B both like the same five films and person A also likes a sixth film that person B hasn’t seen yet, that film becomes a recommendation. This technique doesn’t require knowledge of the content itself—only the user’s behaviour.

Content-Based Filtering works differently. It analyses the characteristics of items you’ve liked in the past and looks for other items that share those characteristics. For example, Spotify may recommend a song with a similar tempo, genre, or mood to the ones you’ve listened to repeatedly.

Hybrid Systems combine both of these methods and often bring in other layers too, like context-aware data. TikTok, for instance, not only looks at what you watch, but how long you watch it for, whether you swipe away quickly and even how long you pause on a video before interacting.

These systems often use machine learning models like decision trees, neural networks, or even transformer-based models to make predictions. These models constantly update as new data comes in. What you liked yesterday might shape what you’re shown today, even if you don’t remember clicking on it.

The Feedback Loop and Personalisation

One of the most interesting and sometimes troubling, aspects of recommendation systems is the feedback loop they create. The more you interact with the system, the more it learns about you. But the more it learns, the more it narrows the scope of what it shows you. Over time, this can lead to a filter bubble, where you see only a small slice of the available content.

This is particularly important on platforms like TikTok, where the ‘For You’ page determines what content goes viral. A single video recommendation can lead to millions of views, shaping what creators choose to post. At the same time, users are nudged into specific patterns. If you watch videos about travel, you’ll see more travel. If you engage with political content, that becomes your feed. This level of personalisation creates a sense of relevance, but also reduces exposure to new perspectives unless the algorithm is specifically designed to introduce diversity.

On Spotify, the system might notice that you prefer calm instrumental playlists during the workday and high-energy tracks at the gym. It adjusts automatically, learning from the time of day, your devices, or even your location. This makes the user experience feel personal and seamless. But again, what it offers is based on what you’ve done before, which means the system works best when your habits are consistent.

Control, Transparency and Design Choices

One area that cannot be ignored in any conversation about recommendation engines is ethics and data privacy. These systems rely heavily on personal data—your viewing history, clicks, swipes, pauses and even device usage. While this data makes personalisation possible, it also raises concerns about how that information is collected, stored and used. Most users are unaware of the extent of tracking taking place and even fewer know how long their data is retained or who has access to it. In the wrong hands, detailed user profiles could be used to manipulate behaviour, reinforce harmful biases, or push content that maximises engagement at the expense of wellbeing. As these algorithms become more central to our digital lives, the ethical responsibility of the platforms behind them grows too. Clearer data policies, stronger user consent frameworks and greater regulatory oversight are all needed to ensure that personalisation does not come at the cost of privacy or autonomy.

Most platforms give users some control over their recommendations, but not much. You might be able to mark a video as “not interested,” or delete your watch history, but the deeper logic remains hidden. This is a design choice, often intentional, to preserve the simplicity of the user experience. Still, as recommendation engines grow in influence, the lack of transparency becomes a bigger issue.

There’s also a business layer to all of this. Recommendations don’t just improve user satisfaction, they drive platform profits. The more time you spend on Netflix, the more likely you are to keep your subscription. The more content you engage with on TikTok, the more ads the platform can show you. These systems are optimised not just for user value, but for platform performance. That’s not necessarily bad, but it does mean the algorithms are not neutral.

Some newer platforms are experimenting with giving users more visibility. YouTube, for example, now shows “Why this video?” explanations that hint at the underlying logic. Spotify Wrapped gives users a digest of their habits. These are small steps, but they reflect growing awareness that people want to understand and the tools they use every day.

Final Thoughts

Recommendation engines are not just features, they are the backbone of the platforms we use most. They shape our choices, influence our habits and in many cases, determine what content thrives. By understanding how they work, we become better users. We can make more conscious decisions, spot patterns in our behaviour and even take steps to reset the feed when it feels too narrow.

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