How TikTok Recommend Videos For You?

Have you ever noticed when you visit TikTok’s feed page, you see a stream of trending videos tailored to your interest? Why does this happen? 

The TikTok “For You” stream is one of its defining features. The idea is to make it simple for users to discover their favourite creators and content. The magical aspect is that each person’s feed is personalised based on their interests. If you see your feed according to your interests, someone else will receive a recommendation according to their interests.

 This article will highlight the “For You feed” process and how it works. Continue reading to learn more about it to make your TikTok “For You” feed more refined and worth watching. 

What Is TikTok Recommendation System And “For You” Feed?

A recommendation engine of TikTok generates the “For You” feed. The recommendation system displays personalised videos on users’ feeds. This system is designed in a way that ensures that users have more customised experiences. 

But the question is, how does it work? It’s not like you just joined TikTok and will start getting tailored feeds. The user needs to follow specific protocols. What are those protocols or factors? Let’s discuss them. 

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Factors Contribute To Personalised “For You” 

The system makes content recommendations after considering user preferences. How does TikTok take into account user preferences? 

User preferences are expressed through these factors.

1. User Engagement

The user demonstrates their interest through the accounts they follow on TikTok, the content they search for or create, and when users like, comment on, or share videos. 

TikTok’s recommendation engine takes note of it and uses it to deliver a customised feed. The user’s preferences signal the TikTok algorithm about users’ choices, and it surfaces there.

2. Video Information

Recommendations on the “For You page” may depend well on the sounds, hashtags, and captions used in TikTok videos. 

3. Device And Account Settings Of User Profile

This customised recommendation may also take into account your preferred or spoken language, your region, and the sort of device you are using. However, because many users do not give account settings much preference, the setting system is given less weight in the recommendation system.

These factors are taken into account by the TikTok recommendation system giving preference to those relevant to the user. Let’s use an example to clarify. A longer video the user watches shows a strong user interest and will be given more weight while recommending a personalised feed. 

The recommendation system gives less weightage to the fact that the video’s creator and viewer are from the same country. The user’s interest is determined by the video during the process, not by the country.

Let’s discuss the characteristics the “For You page” algorithm does not consider when making video recommendations.

• Number of followers

Although a video will probably get more views if an account posts it with more followers, it doesn’t necessarily mean or signal that a video would automatically appear on your stream due to its many likes and creator followers.

• High-performing content in the past

Similarly, suppose an account has previously created high-performing videos. In that case, the number of followers and a high number of likes on a video does not automatically qualify it to show on anyone’s stream.

Creating A Customised “For You Feed”

1. How To Start

You don’t know what you like on TikTok when you first sign up as a user. TikTok lets you choose your interests from categories like fashion, entertainment, art, pets, travel, and more to get started. 

TikTok’s recommendation engine can tailor content to your tastes based on the interests you choose. The app can organize an initial feed solely on your interests when you select your interest from the categories. It becomes even better when you start interacting and making videos on TikTok.

TikTok provides a user with a generalist feed based on trending videos if the user chooses not to choose a category. As soon as you leave comments and engage with the videos, the system will start making early recommendations. Your “For You” feed will get more refined as the app algorithm gets to know your choice better.

2. Explore TikTok

When interacting with TikTok, the system algorithm will better understand your interests. It will make content recommendations based on your interests. The easiest way to curate your feed is to use the app and explore it. TikTok will get better at surfacing recommendations over time that are related to your interests.

The accounts you follow shape Your customised feed. Users can explore the app’s trending themes, effects, filters, sounds, and hashtags to improve recommendations.

3. To Tackle Limited Experience

One disadvantage of tailored feedback is that it prevents users from exploring other stuff. An experience might become constrained if you accommodate too much to someone’s unique preferences, which creates a “filter bubble.” This can result in a stream of videos that is “increasingly homogeneous.”

The TikTok system addresses the issue by filtering out spam, duplicate content, repetitive content, and anything that has been seen before. 

The TikTok system removes repetitive content, duplicated content, content you’ve seen before and spam. Additionally, the app ensures you don’t view consecutive videos from the same creator or with the same sound. For the viewers’ discretion, TikTok won’t recommend any videos that some people may find disturbing.

4. Diversity

You could occasionally see a video in your feed that doesn’t seem to be related to the interests you’ve indicated or has received a lot of likes. This is a deliberate aspect of TikTok’s recommendation strategy. 

By including various videos in your “For You feed”, you have more chances to come across new content categories. It lets you meet new creators and encounter fresh viewpoints and ideas as you browse your feed.

Final Words 

The TikTok team is always trying to improve the “For You” feed to make it more refined and accurate”. In the future, they might construct a robust system that guarantees defences against engagement threats that might affect any recommendation system.