Why do I spend so much time on TikTok? (Pt. 2 How recommendation algorithms work)

In the first part of our exploration into TikTok's captivating nature, we discovered the various types of recommendation algorithms that drive user engagement. Now, in Part 2, we focus specifically on how TikTok's recommendation algorithm operates and why it’s so effective at keeping users glued to their screens.

Understanding TikTok's Recommendation Algorithm

TikTok's "For You" feed is central to the platform's user experience, presenting a personalized stream of videos tailored to individual interests. This personalization is powered by a sophisticated recommendation system that evaluates a multitude of factors to predict and deliver content that resonates with each user. 

According to TikTok, the recommendation system considers several key elements:

1. User Interactions: These are the videos you view or post, accounts you subscribe to, comments you post, and what you make. They serve as pointers informing that this is what you like and what should be taken as what you care about.

2. Video Information: Information such as captions, sounds, and hashtags are scanned to determine the subject of and relevance to the content. For instance, viewing videos that use a particular sound or hashtag can make more content that uses something similar start to appear in your feed.

3. Device and Account Settings: Preferences like language, country setting, and device type are also taken into account, though with less importance than user interaction and video information. These settings are utilized to make sure that content provided is accessible and appropriate for the user.

The algorithm assigns varying weights to these factors. For example, a strong indicator such as watching a longer video from beginning to end would be given more weight than a weak indicator such as the viewer and creator having the same country. This system of fine-grained weighting enables TikTok to optimize content delivery to closely align with personal user preferences.

The Impact of Personalization

TikTok's algorithm tries to present a lot of different kinds of content so that the experience is refreshing and engaging. However, this deluge of personalization is more likely to become 'echo chambers' around themselves, that is, they consume content mostly in feet with what they like or believe. This through limiting exposures to another set of perspectives and content.

Apart from that, TikTok brings together these various engaging themes in their non-formal videos and then populates the feed of a user. This brings the user closer to stumbling upon content, creators, and ideas, ultimately enriching their experience on TikTok.

The Allure of the "For You" Feed

The algorithmic design of the "For You" feed is a key factor in TikTok’s addictiveness. By constantly offering content that aligns with user interests, the app tends to create a seamless and engaging viewing experience that encourages prolonged use. The ability of the algorithm to predict and offer content that speaks to users on an individual basis evokes a feeling of belonging and satisfaction, making it hard for users to quit using the app.

Conclusion

Through utilizing user behavior, device settings, and content information, TikTok's algorithmic recommendation is a powerful tool that creates a highly customized and engaging user experience. Although personalization makes the site more appealing to a wider audience, it also raises questions about user agency and the diversity of the material. Knowing how TikTok's algorithm works can help one better appreciate both the service's user engagement capabilities and the wider ramifications of algorithmic content distribution in the digital era.

Reference List

Geyser, W. (2022). How Does the TikTok Algorithm Work? [online] Influencer Marketing Hub. Available at: https://influencermarketinghub.com/tiktok-algorithm/.

McLachlan, S. (2024). 2024 TikTok Algorithm Explained + Tips to Go Viral. [online] HootSuite. Available at: https://blog.hootsuite.com/tiktok-algorithm/.

Perez, S. (2020). TikTok explains how the recommendation system behind its ‘For You’ feed works. [online] TechCrunch. Available at: https://techcrunch.com/2020/06/18/tiktok-explains-how-the-recommendation-system-behind-its-for-you-feed-works/.

Tiktok.com. (2025a). How TikTok recommends content. [online] Available at: https://support.tiktok.com/en/using-tiktok/exploring-videos/how-tiktok-recommends-content?.

Tiktok.com. (2025b). How TikTok recommends videos #ForYou. [online] Available at: https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you?.