Recommender System

You swore you’d be asleep by 11 p.m. However, somewhere between watching five-minute breakfast ideas and a string of true crime shorts, you lost track of time. Now it’s nearing 2 a.m., and you’re still glued to Instagram Reels, with each clip more gripping than the last. You didn’t plan to keep watching. Yet somehow, every swipe feels like it was made for you.

That’s the hallmark of a recommender system.

What pulls you in isn’t random. It’s the result of a system trained to anticipate what you’ll engage with, based on what you’ve already done.¹ Every time you pause, like, share, or linger just a moment longer than usual, the system registers that signal. From there, it recalibrates and subtly reshapes your feed to keep you engaged the next time you open the app—or even just swipe again.

To most users, this process is invisible. You’re not aware that your taps, rewatches, and hesitations are being interpreted in real time, but they are—and the system is always adapting. Behind the scenes, a recommender system is a form of artificial intelligence (AI), often powered by machine learning.² It draws on big data, which are enormous, continuously updated datasets built from millions of user interactions. The goal is to recognize patterns in behavior and serve up suggestions that feel personal, even though they’re built from collective behavior.

There are three major ways recommender systems operate. One is collaborative filtering, which looks for similarities between users. If people who watched the same dog training videos as you also spent time on a niche baking channel, that channel might now appear in your feed. The logic isn’t based on content: it’s based on clusters of shared behavior.

Another method, content-based filtering, flips the focus. It zeroes in on the characteristics of the content itself—whether it's a video’s tone, subject matter, length, or pacing—and matches those attributes to your known preferences. If you’ve recently interacted with three softly narrated cleaning tutorials, the system might queue up a fourth, even if no one else in your circle has seen it.

Increasingly, platforms use hybrid filtering, which blends the two. These systems track both who’s watching and what’s being watched, then use those layers to refine their predictions. Ideally, these systems surface content that feels familiar enough to catch your eye because it resembles something you’ve liked before, but different enough to spark curiosity so it doesn’t feel like the same thing on repeat.

Of course, the mechanics don’t stop there.

Every scroll you skip, every playlist you abandon, even the videos you replay without liking, all shape the system’s understanding of you. It doesn’t just log what you enjoyed—it infers what you avoided, what bored you, what you’re likely to binge at 1 a.m. versus what you’ll skip without a second glance. 

This process isn’t limited to a single platform. Recommender systems now shape nearly every corner of digital life, from the late-night videos that keep you swiping to the recipes, products, and playlists you didn’t plan to search for. Some rely heavily on repetition, echoing familiar formats and voices until your feed feels more confined than curated, while others aim for discovery.³ When designed with care, they can introduce unfamiliar perspectives, elevate lesser-known creators, and surface content that feels surprising yet still relevant. Sometimes they lead you somewhere unexpected and oddly helpful, even if it wasn’t what you thought you needed.