[chadrs]

I must be rare in that the longer I use tiktok, the less relevant the recommendations feel. Maybe because I compulsively watch videos until the end even if I don't like them.

[dmix]

> Maybe because I compulsively watch videos until the end even if I don't like them.

That would definitely do it, basically destroying their most important signal.

TikTok is best in class for recommendeding content and I personally haven't see a dip in quality. Aka I never get trashy videos or anything cringe, just a consistent stream of science/tech, local Toronto restaurant reviews, cat videos, etc

[throwaway290]

The lack of up/downvote is what destroys their signal, I think. They do it to themselves

[MuffinFlavored]

I want to know what portion of the algorithm is responsible for, when you are given a new blank slate user, "tries" certain categories

like, let's present this user travel or cooking material, that's usually safe

then, let's try things like certain genres of music, we'll see what they like/don't like

what i don't get is... how does that first recommendation on the #foryoupage or discover or whatever it's called, starts recommending you the sex workers who try to post as close to NSFW material as possible, get you to land on their profile, in their bio is a link to their Instagram or Linktree, and then from there it's an OnlyFans link

does the system try to recommend a soft entry into this content and then just pivot away if the user doesn't like it?

[Brystephor]

TikTok likely has enough information about others that it can begin to build a profile about you from the moment you login.

Let's use a hypothetical scenario: Someone states that they identify as a man, they're in the 20-25 year old age range, and based on phone location you can gather that they live in Texas. Now you're labeled as a 20-25yo Texas Man. Then you can look at others who fall in the "20-25yo Texas Man" category and show things you'd expect that group to like because chances are, you're more similar to others in the group than being a true outlier. If other people in the "20-25yo Texas Man" group have expressed interest in Apples, NSFW material, and lawn mowing videos, then since you're in that group, it's going to start off with that same material.

disclaimer: i've never signed up for tiktok and have no clue if this is how they do it.

[pimlottc]

The classic terminology for this in AI/ML is "explore vs exploit", i.e. striking a balance between trying new things (in hopes of finding a new favorite) vs going back to the tried-and-true.

[nanidin]

I have noticed these types of videos slip into Facebook’s Reels late at night. It’ll switch from showing me people making candy and doing home improvement stuff - videos with tens of thousands of views and likes, then it will cut to a video with almost 0 views/likes that are basically the beginning of the Onlyfans sales funnel. Never when the sun is up!

[bluefirebrand]

It probably just recommends a bunch of stuff that is popular at the moment for new users.

Or it tries to match you to an existing profile it has from some ad network data or something.

[jabbany]

Seems like a common problem of recommenders tbh.

Like binary search, they're really good at finding local optima quickly, and then are rather bad at getting out of them once they get there.

[realfeel78]

Sounds like a you problem? At any rate, they added an option to reset it anytime a few weeks ago:

https://techcrunch.com/2023/03/16/tiktoks-new-feature-lets-y...

[joshu]

long-press and select “not interested” and it will figure it out pretty quickly.

[gammarator]

This is a critical step to get rid of bad recommendations, and the algorithm seems to treat it as a very strong signal—after 1-a few “not interested” labels you won’t see that content for weeks if ever.

[winternett]

That's because on the inverse of algorithm recommendations, everyone forgets that the platform is primarily built around an ad system that generates money... There is always going to be a conflict with recommendations because they need to placate and accommodate paying users of all kinds, even users with low quality content and outright commercials. Pretty much all of these social platforms work in this manner now. As a result everyone is seeing undesired (ad) boosted content on top of the other ads (that are marked as ads).

[thomasahle]

They are probably trying to save GPU power on already hooked users. This is a common trick in Recommendation Systems. You want to spend the most resources / run your most expensive model on users that are just checking out your platform.

A bit like how Poker sites give you better cards in the beginning.

[osti]

>A bit like how Poker sites give you better cards in the beginning.

As a former online poker pro, this is probably the most idiotic thing I've read on this site.

[nickthegreek]

This can happen to me if it gets stuck down a avenue that it thought I was interested in. But the next day or even a few hours later, it seems to correct itself.