1.2 TikTok/Reels Algorithms
The TikTok and Instagram Reels algorithm is the most advanced mechanism for capturing human attention to date. It can be compared to an experienced hypnotist who, within the first 30 seconds of meeting you, identifies your deepest preferences and vulnerabilities, then uses them to make you unable to look away from the screen.
The fundamental principle differs from YouTube. While YouTube analyzes your long-term viewing history (what you searched for a week ago), TikTok operates in real-time. Its main question is: "Based on what you did RIGHT NOW, what should we show you NEXT to keep you scrolling?"
How Your Initial "For You Page" (FYP) is Formed:
1. Cold Start
A new user has no history. The algorithm looks at their metadata: language, country, device, time of day. It serves a "universal starter pack" — 20-30 of the most viral videos in that region among a new audience. This is necessary to collect the first signals.
2. Every Interaction is a Vote
The algorithm is a giant voting system, where you vote not with a button, but with your behavior. The weight of the votes is distributed as follows (in descending order of importance):
- Watching to the end and replaying. The strongest signal. Means the content was a perfect hit.
- Scrolling past within the first second. A "not interested" signal. The algorithm will remember both the visual and audio patterns of that video to show similar content less often.
- Like, saving, commenting, replying. Explicit signals of approval.
- Following the creator. A long-term commitment to a topic.
- Pausing on a specific frame. A signal of detailed interest in something in the video.
- Sharing. The maximum social validation of content.
3. Deconstructing Content
While you are liking, the algorithm "scans" the video itself, creating its digital fingerprint:
- Visual analysis: Objects in the frame (a cat, a face, a car), colors, shooting style (static shot, dynamic editing), presence of text and subtitles.
- Audio analysis: Music (its rhythm, genre, popularity), the narrator's voice, sound effects.
- Text analysis: Hashtags, description, on-screen text.
- Creator metadata: Their geolocation, gender, engagement statistics.
4. Building a Multidimensional Interest Map
The algorithm doesn't cram you into a single category ("cat lover"). It builds a vector of your interests in a space of thousands of topics. You can simultaneously have a high "weight" for topics like: [pets: 0.9], [programming courses: 0.7], [gadget reviews: 0.6], [stand-up comedy: 0.5]. After each of your actions, these weights are adjusted.
Why is This Addictive? The "Rabbit Hole" Effect
The algorithm masterfully applies the technique of "testing and gradient descent."
Example:
- You watched a video about a Maine Coon cat to the end. The algorithm makes a hypothesis: "The user likes big cats."
- It tests the hypothesis: shows you three more videos with Maine Coons.
- You watch two of them to the end. The hypothesis is confirmed.
- Now the algorithm begins to "narrow the focus": "Which specific aspects of Maine Coons are interesting?" It starts varying the context: Maine Coons with other animals, Maine Coons in funny situations, Maine Coons and small children.
- You spend more time on videos where the cat plays with a child. The algorithm records: "Interest is not just in the breed, but in the social interaction of the animal with a human."
- Now your feed can smoothly shift towards content about children and animals in general, then to family content, and from there to educational psychology. Within an hour, you can go down a "rabbit hole" from funny animals to deep pedagogy, and each step will feel natural.
Technical Features That Make the Algorithm Formidable:
1. Hyper A/B Testing
The same video is shown to different micro-groups (the first 100, 1000, 10,000 viewers) with different starting conditions. The algorithm observes in which group it "took off" (high retention rates), and starts showing the video to a broader audience with characteristics similar to that group. A video doesn't become popular on its own—the algorithm finds its ideal audience for it.
2. Explosive Virality
If a video performs well with its micro-group, it starts being shown to a wider audience with similar behavioral patterns. This process can be exponential, creating the impression of a "sudden" video explosion.
3. Anti-Looping System
To avoid trapping a user in an information bubble, the algorithm periodically (about 1 in 20 videos) throws in an "outside world signal" — content that doesn't match your current interests but is popular on the global feed. Your reaction to this "signal" updates your interest map.
Ethics and Manipulation:
The algorithm is optimized for one metric — total time spent in the app (Time Spent). It does not aim to make you smarter, happier, or more informed. Its goal is maximum retention. Therefore, it unconsciously leans towards content that evokes strong emotions (surprise, outrage, nostalgia, a sense of community) and can amplify polarizing or addictive topics.
This makes it not just a recommendation tool, but an active architect of human experience, which learns from our reactions in real time and restructures the offered reality so that we don't want to leave it.