Classification on a binary basis ("that person will like that video or not") is on a much better ontological basis than multiple class classification ("out of 50 links, that person picked will pick the 39th") For one thing it is possible to calibrate a yes/no decision, TikTok knows that there is an 80% chance that you're going to like this video and a 30% chance that you are going to like that video and make a rational decision about what to show you. Multi-class classification is a shot in the dark in comparison.
Google and everyone else in social media wants to look like the image in
https://tvtropes.org/pmwiki/pmwiki.php/Funny/Idiocracy
They give you 50 things to click on and make a big deal about having a privacy policy and how "data is the new oil" but the clickthrough data is absolutely worthless because out of 50 things there were 5 things you might have clicked on and you could only pick one. In particular, they can't come to any conclusion that you didn't like any of the other 49 things.
The HR office hires one black woman for their DEI initiative to work in "AI Ethics" to claim this technology is so powerful that it's actually dangerous and when they get into some spat with management and get fired they are doing their job because it legitimizes the idea that this "artificial intelligence" is so powerful it's dangerous but the web brought to you by Google, Facebook, Twitter and such is artificial stupidity inspired by Idiocracy just as Facebook's metaverse is inspired by Sword Art Online.
TikTok on the other hand really is dangerous, not because it's algorithms are better (they are, see https://en.wikipedia.org/wiki/Multi-armed_bandit) but because their whole approach gives them meaningful data that can be put to work. It vindicates the idea of looking at one piece of content a time or a "Tinder for documents" that I talked about here https://ontology2.com/essays/ClassifyingHackerNewsArticles/
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