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IceMetalPunk | 3 years ago
I think the reason Google (and Amazon, and Netflix, and every other major tech company) uses clicks/view time as recommendation engine inputs is because... well, what else can they use? What quantifiable metric could possibly be used for large-scale, automated recommendations that more accurately indicates what someone actually wants to see more of? (This isn't rhetorical: if you have any ideas, I'd love to hear them.)
I don't think clicks/view time are the best metrics at all, and I don't think they're extremely accurate. But I also think they're the most accurate measure we've got, with the only other options being either (a) remove recommendations entirely, or (b) have humans manually monitor everything you do on the website, occasionally ask you why you clicked or watched things, and then make personal recommendations to you based on your answers. The latter of which is slow, more expensive, less scalable, more invasive to the user, and more tedious for the employees that would have to sit there monitoring you.
Maybe one day we'll have an AI method of using your comments and search terms as a better indication of desire (I dare say some of the recent LLMs are close to being ready for that task), but we're not there yet.
saurik|3 years ago
In the case of social networks, I think they served a positive function to both society and the people who used them back when they didn't have the recommendation algorithms, and your feeds were curated by you choosing to follow people explicitly; this, however, was not profitable, so we are now here.
If you are to do it, then yes: I think you probably need to do what TikTok is doing, and have humans heavily involved in the recommendation system in a way that attempts to put a hand on the wheel rather than the Google way to approach problems with algorithms on top of algorithms and no humans anywhere.
danaris|3 years ago