TikTok’s “Who to Follow” recommendations are generated through a complex algorithm designed to connect users with content and creators likely to be of interest. The platform analyzes various factors, including existing connections, user interactions, content engagement, and device information, to suggest potential accounts to follow. For example, if a user frequently watches videos related to cooking and has several friends who follow a particular chef, that chef’s account is likely to appear as a suggestion.
These recommendations serve multiple purposes. They enhance user engagement by introducing individuals to relevant content, which can increase time spent on the app. Furthermore, they facilitate community growth by connecting users with shared interests, fostering a sense of belonging and interaction. Historically, these types of recommendation systems have evolved from simple collaborative filtering techniques to sophisticated machine learning models, reflecting advancements in data analysis and predictive algorithms.
