The app is designed in a way that basically everything you do results in a strong positive or negative signal for a video that the recommendation algorithm can use.
Take Youtube, a video site that added suggestions. If you're logged in, you get dumped on a homepage with an endlessly scrolling list of recommendations. You scroll until a thumbnail or video title catches your eye, you watch that video, might like or comment, then click one of ~10 recommendations. The algorithm gets a positive signal based on what you click and your watch-time, but the negative signal on anything you don't click is incredibly weak. Because you choose what you watch, positive signals are mostly driven by titles, thumbnails and familiarity, and negative signals are rare. As a result, most recommendations are stuff you know or things adjacent to it (people who watch this also watch this), and quickly get boring. The recommendations can't inject novelty or purposefully expand the algorithm's knowledge.
Now on Tiktok: You open the app, you get a single video in full screen. Either you scroll on (negative signal) or you watch it until the end (positive), or you let it repeat (very positive). Or you might like or comment. After scrolling on, you get another video in full screen, same game again. The cost of a bad recommendation is low because videos are short and quick to judge, so the algorithm can occasionally give you videos it's uncertain about or that are selected completely randomly. Your reaction will give a signal, no matter what (either you watch till the end or you don't) and as a result the algorithm knows more about you and about the video.