Hacker News Reader

Comment by phailhaus | Hacker News Reader

Great! But nothing is going to change until people realize that the problem is the feedback loop. It's not the recommendation engine itself, it's the fact that there's no way "out" of the feed that the engine produces. It recommends you stuff, you have little choice but to engage with it, and then it trains on that information.

This is the problem with most of social media today. It is a very well known problem in ML [1], but nobody is willing to do anything about it because it's a fundamental UX change. Facebook, Twitter, YouTube, TikTok, they have defined themselves by their recommendation engines.

[1] https://towardsdatascience.com/dangerous-feedback-loops-in-m...

View on HN

reminds me of a story about a guy who was given a gift, a decorative plate with a rooster on it i think it was. didn’t care for it too much, but out of politeness put it on display on an empty cabinet he had. a while later someone noticed he had it and figured he liked it, so got him a similar decorative plate with a rooster on it. again, out of politeness, he put it next to the old one. now other people started to think he just really liked roosters, and started giving him little rooster statues and nicknacks. Eventually he just has a whole display cabinet of rooster themed gifts that he never really cared for to begin with, but people just assume he likes them because people keep giving them to him.

I have a whole shelf full of horse stuff because of this!~

https://www.smbc-comics.com/comic/critical

this is a hilarious example for a serious problem. well done!

and in the end, he starts to like that decorative plates with roosters or at least thinks rooster on plate is a nice decoration if it is produced that much.

I had a friend who this happened to with ducks. You would go to her house and there were little duck icons and duck figurines and duck themed things everywhere.

When questioned, it turns out she never cared about or liked ducks. Someone gave her one and then it became the go to present for her for every occasion, for decades.

Decades of ducks.

I am probably part of this problem - an enabler, so to say. I was hosted a nice lady with a house full of duck themed things (this was in southern England, probably not the same lol), and left her two (rather cute) duck-themed things when I moved assuming that she'd like those.

She was unequivocally happy when I did, but perhaps it was just because of the gesture.

(EDIT: also, the rooster/horse/duck example of feedback loop would make for a terrific blog post)

I think Instagram in particularly is bad in this regard. It seemingly becomes convinced that I care deeply about the subject of any post that I even momentarily linger on.

Don't you have a choice.. to not engage with it? If you didn't like it then assuming the metrics system is working correctly, this would be negative feedback to the ML model, causing said content to not be shown in the future.

Couple problems:

1. Actively supplying negative feedback is sometimes hidden behind secondary menus, making it much higher friction compared to just...scrolling past. So most users don't spend the effort. Even with a dislike button, it's unclear what the system is learning. It can't know that I don't like this particular video because it's a conspiracy theory, and to stop showing me those. These platforms often don't even support explicit categories, so how would they know?

2. It's extremely high friction to teach the algorithm you're interested in something that it doesn't suggest to you! There's the whole unknown unknowns problem: how do you teach the algorithm you're interested in something that you've never seen before?

I still think Reddit has handled this the best. No system is perfect, but Reddit's challenges are much more manageable than the quagmire that TikTok, Facebook, and YouTube have gotten themselves into. I can just unsubscribe from r/conspiracy, and I'm out. Basically impossible to teach that to YouTube without weeks of careful curation. They think they're smart enough to know what I like, but they're not and never will be.

Twitter could recreate a similar system: 1) auto-tag tweets with labels, rather than users needing to submit to subreddits 2) auto-sub most people some default set of labels 3) let them un-sub if they want.

They just don't want to.

Auto-tagging is not a business they want to be in, because they will invariably get it wrong sometimes and people will start drama over it. And they'd be right to: human communication is nuanced and you can't just "guess" what someone means within 280 characters.

People already use hashtags, what would happen if you could subscribe to them? It would create a whole new way of interacting with the platform, something closer to Reddit where people post to streams. But the fact that these streams can't be moderated (or even downvoted) might be a nonstarter.

Wait, Twitter already has this in the form of Topics. It's very coarse-grained, but still.

Regarding 1. I would expect like rate on recommended tweets to be a better signal than impressions.

Eh, 1 is sort of why I see implicit negative feedback as more useful here. Namely, tracking the duration a user is probably giving their attention to a given item, weighted in accordance with how long you'd expect someone to give their attention to an item based on how long it is.

For example, I might see a some specific word or pattern of words in a tweet and quickly skip to the next one. That's very low friction but also a powerful signal. There are drawbacks (e.g. the long boring video that looks like something is about to happen but never does), but that's mitigated by combining this with other signals.

With 2, it's an explore/exploit trade-off. You try to explore as far and wide as you can while trying to avoid the things the user may dislike, all while sprinkling in just enough of the stuff that you know they'll like.

You're talking like an ML engineer. :)

Yes, there are signals in human behavior that you can feed to your model. But no, it is never going to learn "on Mondays he works on his comics, so he'd prefer to see webcomic-related content" Don't sprinkle in your explore/exploit experiments. I know what I want, just let me decide based on what I'm in the mood for!

TikTok-style feeds are the absolute worst offender here, where they couldn't care less about what you think. They will serve you content, and you will either say "yes" or "no". So the only option for you as the user is to just wander through their content hyperspace. There's no structured way to jump between topics because everything lives in this formless content soup.

The other problem is that many social media platforms have you subscribe to the content streams of individuals directly. Individuals are high variance. How can you teach these engines that "I only care about this person's posts about pianos, not their terrarium hobby."

> Namely, tracking the duration a user is probably giving their attention to a given item,

I pay a lot of attention to classes of things I don't want to see at all.

Then, according to the algorithm, you do want to see them.

That's exactly the problem! I don't want to see them, but the algorithm refuses to ask me.

If they think that the length of time I spend on a post is 100% correlated with the amount that I want to see it, they really don't understand people at all. It's an embarrassingly shallow model of how how humans behave.

> It's an embarrassingly shallow model of how how humans behave.

It's a trade off. Viewing rates give back feedback 100% of the time. Asking users for a thumbs up or down gives feedback almost none of the time, and still might not be accurate.

Thank you! Working on a concept for a big org on what may become a large ML-based system one day. I knew about this feedback loop issue, but was too dumb to actually remember and face this problem. :) It's all over today's rec engines -- and yet, just like the things we're not shown in these systems, the problem itself seems to become invisible. Because it requires new thinking.

Worth exploring.

Reddit is a good example of an alternative system, and it works because subreddits serve as lower-variance composable streams as well as shared digital spaces. Even though your main feed is partially driven by a recommendation engine, it never contains any content that you didn't subscribe to (ignoring ads, of course). Instead, you may see a "posts from other subreddits" section that breaks up the feed explicitly to offer alternatives. If you're interested in a particular topic, you can drop into that subreddit, where you'll see pretty much the same view as everyone else. In all these cases, you're always in control.

I wouldn't be surprised if Reddit actually has the best data on their user's preferences, because they let them explicitly decide what they want rather than playing this "hot and cold" game like TikTok. Those platforms also only let you subscribe to individual accounts, so they have to infer what your interests actually are.

I feel like the Youtube one is good. You can mark videos and channels as "not interested" and Youtbe really knows me due to my account age and usage... It recommends me unknown videos and I tend to like them but also more mainstream stuff.

It doesn't matter how good they try to make their recommendation system, they will never know you like yourself. For example, when I go to the YouTube home page, there is a list of categories at the top that it's identified for me. I didn't choose these. I can't add or remove them myself if it's wrong. I just have to hope that I watch the "right videos" and it picks up on a new interest that I have.

But I already know what interests I have! I want to have videos about terrariums on my home page now, not in a week when I've watched enough. This is what I mean by recommendation systems not being good enough. They need to give the user more control over what they want to see, because they can never read my mind. Their recommendations will get even better with that information!

Isn’t the “I’m not interested in this tweet”, “Show me fewer tweets from X”, etc options working for you? They seem to have an effect on my end.

What did they learn though? They can't possibly know why I'm not interested in that tweet, because there's just not enough information. So all they'll do is stop showing me tweets from that one person. No way to express "yeah I'm interested in his tweets about music, not gardening." The problem is that mostly following people means that you can't teach the algorithm about your particular interests.

Topics are better, but there doesn't seem to be a way to make sure your tweet ends up in a particular topic. Again, you have to pray to The Algorithm that it will label tweets correctly. And if your particular interest isn't a Topic, you're out of luck.

So , all we need to do is to open source humans?

Haha no, we just need to stop pretending as if ML can read our minds. It's a tool, it's good at recommending me content, but it will never know what I'm in the mood for on a particular Friday night.

Reply on news.ycombinator.com