Comment by FullstakBlogger
7 months ago
15 years ago, I used to keep many tabs of youtube videos open just because the "related" section was full of interesting videos. Then each of those videos had interesting relations. There was so much to explore before hitting a dead-end and starting somewhere else.
Now the "related" section is gone in favor of "recommended" samey clickbait garbage. The relations between human interests are too esoteric for current ML classifiers to understand. The old Markov-chain style works with the human, and lets them recognize what kind of space they've gotten themselves into, and make intelligent decisions, which ultimately benefit the system.
If you judge the system by the presence of negative outliers, rather than positive, then I can understand seeing no difference.
>The relations between human interests are too esoteric for current ML classifiers to understand.
I would go further and say that it is impossible. Human interests are contextual and change over time, sometimes in the span of minutes.
Imagine that all the videos on the internet would be on one big video website. You would watch car videos, movie trailers, listen to music, and watch porn in one place. Could the algorithm correctly predict when you're in the mood for porn and when you aren't? No, it couldn't.
The website might know what kind of cars, what kind of music, and what kind of porn you like, but it wouldn't be able to tell which of these categories you would currently be interested in.
I think current YouTube (and other recommendation-heavy services) have this problem. Sometimes I want to watch videos about programming, but sometimes I don't. But the algorithm doesn't know that. It can't know that without being able to track me outside of the website.
>I would go further and say that it is impossible. Human interests are contextual and change over time, sometimes in the span of minutes.
Theres a general problem in the tech world where people seem to inexplicably disregard the issue of non-reducibility. The point about the algorithm lacking access to necessary external information is good.
A dictionary app obviously can't predict what word I want to look up without simulating my mind-state. A set of probabilistic state transitions is at least a tangible shadow of typical human mind-states who make those transitions.
I think there are things they could do and that ML could maybe help?
* They could let me directly enter my interests instead of guessing
* They could classify videos by expertise (tags or ML) and stop recommending beginner videos to someone who expresses an interest in expert videos.
* They could let me opt out of recommending videos I've already watched
* They could separate sites into larger categories and stop recommending things not in that category. For me personally, when I got to youtube.com I don't want music but 30-70% of the recommendations are for music. If the split into 2 categories (videos.youtube.com - no music) and (music.youtube.com - only music) they'd end up recommending far more to me that I'm actually interested in at the time. They could add other broad categories like (gaming.youtube.com, documentaries.youtube.com, science.youtube.com, cooking.youtube.com, ...., as deep as they want). Classifying a video could be ML or creator decided. If you're only allowed one category they would be incentive to not mis-classify. If they need more incentive they could dis-recommend your videos if you mis-classify too many/too often).
* They could let me mark videos as watched and actually track that the same as read/unread email. As it is, if you click "not interested -> already watched" they don't mark the video as visibly watched (the red bar under the video). Further, if you start watching again you lose the red-bar (it gets reset to your current position). I get that tracking where you are in a video is something that's different for email vs video but at the same time (1) if I made it to 90% of the way through then for me at least, that's "watched" - same as "read" for email and I'd like it "archived" (don't recommend this to me again) even if I start watching it again (same as reading an email marked as "read)
Those are some good suggestions, particularly the first one:
>let me directly enter my interests
2 replies →
you can click one of the ML-selected categories at the top of your homepage to tell it what you'd like to see today
They probably optimize your engagement NOW - with clickbaity videos. So their KPIs show big increases. But in long term you realize that what you watch is garbage and stop watching alltogether.
Someone probably changed the engine that shows videos for you - exactly as with search.
I have to say, all my YouTube recommendations are good and they're rarely clickbait. If you sign out they're pretty bad though.