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Algo explained: How streaming services decide what to recommend to you
01st July, 2026 09:05 PM
Every evening across the world, millions of people sit down to unwind with their phones.
Whether scrolling through TikTok during a matatu ride, listening to Spotify playlists, or streaming a movie on Netflix, the content on the screen always seems to perfectly match the mood.
It feels like magic, but it is actually the result of smart computer systems called algorithms working behind the scenes. Understanding how these platforms choose what to show can help readers take back control of their screen time.
The silent trackers
Streaming services do not just guess what people like. They use advanced systems called collaborative filtering and deep learning to study online habits. The apps track much more than just the videos or songs that get clicked.
They measure how many seconds a user pauses on a thumbnail, exactly when they close a video, and the specific time of day they open the app.
A peer-reviewed research paper on Deep Learning based Recommender System notes that “collaborative filtering makes recommendations by learning from user-item historical interactions, either explicit (e.g. user’s previous ratings) or implicit feedback (e.g. browsing history).”
For the average user, this implicit feedback is what matters most. The system looks at these quiet habits and groups people with thousands of other users who behave the exact same way. If similar users enjoy a specific video, the app pushes it to everyone else in that group.
Taking charge
The primary goal of these apps is to keep people watching for as long as possible. This code has a massive impact on daily life.
In a study published in the ACM Transactions on Management Information Systems, researchers Carlos Gomez-Uribe and Neil Hunt revealed that the recommendation system “in total influences choice for about 80% of hours streamed at Netflix.”
This influence means algorithms heavily shape local entertainment choices, news consumption, and trends. Fortunately, knowing this gives internet users real power.
Since the machine learns entirely from past actions, readers can train the system to show better options. Quickly skipping boring videos, clearing watch histories, and avoiding sensational thumbnails forces the algorithm to reset its math.
