Inside TikTok's Killer Algorithm #
Sara Fischer, wrote a great piece at Axios about the algorithm that powers TikTok. I found it fascinating, simply because I go on TikTok quite a lot, and I must admit they always seem to surface videos that I like.
She wrote about why the algorithm matters, the current situation with TikTok in the U.S., and other updates about the privacy and security practices.
I won't regurgitate everything here, but unsurprisingly, the bit I found most interesting was how it worked:
How it works: TikTok's algorithm uses machine learning to determine what content a user is most likely to engage with and serve them more of it, by finding videos that are similar or that are liked by people with similar user preferences.
When users open TikTok for the first time, they are shown 8 popular videos featuring different trends, music, and topics. After that, the algorithm will continue to serve the user new iterations of 8 videos based on which videos the user engages with and what the user does.
The algorithm identifies similar videos to those that have engaged a user based on video information, which could include details like captions, hashtags or sounds. Recommendations also take into account user device and account settings, which include data like language preference, country setting, and device type.
Once TikTok collects enough data about the user, the app is able to map a user's preferences in relation to similar users and group them into "clusters." Simultaneously, it also groups videos into "clusters" based on similar themes, like "basketball" or "bunnies."
Using machine learning, the algorithm serves videos to users based on their proximity to other clusters of users and content that they like.
TikTok's logic aims to avoid redundancies that could bore the user, like seeing multiple videos with the same music or from the same creator.