191–200 of 317 posts
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Regarding Amazon: "Oh, you bought a wallet? Here are twenty wallets of the exact same type but with slightly different colors that you might want to buy, too"
It's probably correct by expected value. You're just not the multiple wallet type. I'm sure there are people who'd want five wallets, eight different shoe styles, multiple cuts of pants... Your indifference is subsumed in the sheer size of others' consumption.
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This is because they're not trying to give you stuff you'd generally like given your entire history. The recommendation feature powers the part of YouTube that auto-plays the upcoming video. So e.g. if I go and view Russian dashcam videos they're going to automatically play more of them, even though I've shown no prior interest in that topic. Having two systems for recommendations would introduce a lot of UI complexi…
Having two systems for recommendations would introduce a lot of UI complexity, Do you mean, in terms of implementation or for the user? Because as the later, I think the two are already conceptually different (homepage vs next video), so I don't see how just feeding it different videos would make it more complex.
So let's say I'm interested in exactly two things. Russian dash cam videos and videos of trains without narration.
I go to the homepage and click on one type of video, what should the next video be? A random video from the two categories? One or the other? Should it show me a banner at the top indicating what recommendation mode I'm in (historical preference or "similar to current video").
Now I go and do the same on my Android YouTube app which has no real UI equivalent of a homepage, what happens then?
This is a lot of UX complexity for a feature few probably care about.
Facebook also has a serious problem in that its news feed is a content recommendation engine with only positive reinforcement but no negative reinforcement. So you end up with a ton of false positives even when actively interacting with the content, and their system doesn't even know how wrong it is. And should you really not like some content, the solution is unfriending the poster, rather than simply matching again…
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Youtube is definitely over-weighing the last few videos. Then again, it's likely optimized for a different market segment. Especially with how many children are given tablets. Like, brief intense fascination with many subjects in general is genuinely hard to optimize for, especially when a huge portion of your market has long-lasting intense fascination with a few subjects.
If I watch a video I ended up not being interested in, I delete it from my watch history to avoid YouTube spamming me.
I find a lot of sites feel like they're overtuning their recommendation engines, to the detriment of using the site. YouTube is particularly bad for this - given the years of history and somewhat regular viewing of the site, I feel like it should have a relatively good idea of what I'm interested in. Instead, the YouTube homepage seems myopically focused on the last 5-10 videos I watched.
This seems like a good algorithm to me, as when i'm watching skateboard videos with my friends, I don't want "how to caulk tile joints" to show up
>.. shame on Facebook for torturing them for the exposure when I was originally targeting maybe 10 other colleagues to begin with. Seems like the facebook algorithm is actually working for the users by in effect blocking insipid idiots from posting their crap trying to game the system. You don't 'target' colleagues.. colleagues are people you work with and respect.. not try to spam.
Geez. Target in this case: stuff that will most probably interest that specific group of people. How about reading the authors' publications before making assumptions next time?
>Could you fix this algorithm problem please? I’m sure I’m not the only son or daughter to suffer from it.
He's asking facebook to fix his mom? The algorithm isn't broken.
Facebook also has a serious problem in that its news feed is a content recommendation engine with only positive reinforcement but no negative reinforcement. So you end up with a ton of false positives even when actively interacting with the content, and their system doesn't even know how wrong it is. And should you really not like some content, the solution is unfriending the poster, rather than simply matching again…
Is that also true to HN, since most users can't downvote?
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Regarding Amazon: "Oh, you bought a wallet? Here are twenty wallets of the exact same type but with slightly different colors that you might want to buy, too"
For books this works pretty well for me as I tend to read genres in bursts. If I just read a scifi books, I'll read a bunch more before going back to fantasy ;)
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Regarding Amazon: "Oh, you bought a wallet? Here are twenty wallets of the exact same type but with slightly different colors that you might want to buy, too"
Funny story: A while ago I took an Ayurveda class (Indian medicine) and part of it was doing an oil enema. There are devices for this that are also used by a certain population for sexual pleasures. Bought one on Amazon and for months I couldn't show my Amazon page to other people because it was filled with sex toys....
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Netflix, I think, has killed their own recommendation algorithm when they removed stars and made it boolean. I don't know if those buttons even do anything anymore because I think they're just matching based off demographic and who they're trying to market to now. It's recommending shows that I never would watch in a million years and giving them high matches despite me disliking most similar shows just because I'm a…
I think Netflix probably did it because people are inherently bad at being objective. For the average person, 2 stars for a movie and 4 for another isn't based on anything measurable, even they couldn't explain. I'm shocked at some of the amazon product reviews, most of which are 5 star reviews even if the product is absolutely terrible. Movies are different than products, but it's the same people doing the reviewing…
Their % match numbers are fairly accurate, but I have had to go into the watch history and delete the occasional movie watched and finished that we actually hated. No number of 1-stars (or thumbs downs) would eradicate its effects on the recommendations.