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a) they really don't need to know the content, they can infer most of the content by 'who' watches and how much, aka collaborative filtering. b) Shouldn't bias

a) they really don't need to know the content, they can infer most of the content by 'who' watches and how much, aka collaborative filtering. b) Shouldn't bias towards who "posts often" esp if the videos are shit. "Hooked the longest" would be ranked highly if you get hooked like the 'hooked users', makes sense? c) those are poor systems, and actually, I think amazon has one of the much better rec systems. Youtube has one of the worst. Just today their 4th ranked vid for me was the 2 hr long 9/11/2001 broadcast. Wat? Then, they never boost new videos of a channel I subscribe to and have seen every video of theirs over the last 3 months. I literally have to check the channel most recent vid list daily to see if I missed something. I think youtube's weak recommender system is more a result of them having a hammer(deep learning) and seeing every problem as a nail.

doh 10y ago
a) That's the general problem. If you are being encapsulated by the algorithm, there is no way out of the bubble so it doesn't help at the end that they use collaborative filtering, because majority of people ends up watching the same things. b) They chose the metric that made the most business sense. That's why if you spend 6 months working on your video and somebody else produces one video a day, you'll never show up anywhere close to top of the suggested videos. c) Agree. Amazon has one of the better ones, but it's still terrible. You hit the nail on its head with your assumption. This is in general Google's approach. But even with deep learning, the system is heavily biased, intentionally.
- eanzenberg 10y ago
  On a) if the vast majority of users end up watching the same videos then they wouldn't need a fancy rank system and instead rank videos by most viewed per day, or something like that. My gut feeling is there's ways to segment users by their views and infer many characteristics of the videos a user wants to watch by looking at what similar users watch. It's still highly dimensional though. b) My guess is they're optimizing for viewership engagement with the added side-benefit of video-creator engagement.

doh 10y ago

a) That's the general problem. If you are being encapsulated by the algorithm, there is no way out of the bubble so it doesn't help at the end that they use collaborative filtering, because majority of people ends up watching the same things. b) They chose the metric that made the most business sense. That's why if you spend 6 months working on your video and somebody else produces one video a day, you'll never show up anywhere close to top of the suggested videos. c) Agree. Amazon has one of the better ones, but it's still terrible. You hit the nail on its head with your assumption. This is in general Google's approach. But even with deep learning, the system is heavily biased, intentionally.

eanzenberg 10y ago
On a) if the vast majority of users end up watching the same videos then they wouldn't need a fancy rank system and instead rank videos by most viewed per day, or something like that. My gut feeling is there's ways to segment users by their views and infer many characteristics of the videos a user wants to watch by looking at what similar users watch. It's still highly dimensional though. b) My guess is they're optimizing for viewership engagement with the added side-benefit of video-creator engagement.