How TikTok's design makes the algorithm work
eugenewei.com
eugenewei.com
I don't want to sound overly dismissive, but it's pretty clear reading this that the author has not worked on recommendation or personalization systems recently.
The high level description of how he believes TikTok works is probably accurate (I don't work there so I can only speculate), but it's not unique to TikTok. Also most of the details are probably not accurate.
Some examples:
> For its algorithm to become as effective as it has, TikTok became its own source of training data.
^This is true of nearly every recommendation system.
> If you click into a text post by someone on Facebook but don’t comment or like ... That negative sentiment is difficult to capture
You can do the same thing with comments that he claims TT does with video (dwell time threshold feature)
> As you scroll up and past many stories, the algorithm can’t “see” which story your eyes rest on.
I don't work at Twitter, but I doubt that matters very much.
Also regarding "negative sentiment" feedback from users. TikTok does not have a UI tool to say "I don't like this", but other apps do. For example, you can ask Instagram to "show me fewer posts like this".
I agree with the claim that the "one thing at a time" UI is nice for the user in a lot of ways. But as a person who builds these things, I don't think this makes much of a difference for machine learning.
For me at least, tiktok.com is an infinite scroll.
In fact, imgur's desktop website comes to mind as the rare design pattern where you can (or, could, back when it was only gifs) consume the content in a gallery view where the application doesn't know what holds your attention.
Press and hold on TikTok shows a popup for this.
But yeah I think this makes it pretty clear that TT is not doing anything qualitatively different (except possibly a massive army of human annotators). They just do a good job.
But TikTok is definitely special in this regard.
If you actually use TikTok for any length of time, you can see how remarkable it is. It learns extremely quickly what you want to see (and what you don't want to see), and provides it accurately and in apparently infinite quantity.
The only difference being TikTok uses a lot more human moderation compared with YouTube.
I don't find their recommendation system any special as opposed to their specific operational model.
>I don't work at Twitter, but I doubt that matters very much.
>Also regarding "negative sentiment" feedback from users. TikTok does not have a UI tool to say "I don't like this", but other apps do. For example, you can ask Instagram to "show me fewer posts like this".
There's a big difference in the usefulness of implicit and explicit feedback for recommenders, and there's a long history of research trying to address the shortcomings of each, or combine them to avoid their weaknesses (e.g. research into presentation biases, augmented matrix factorization, MF with implicit data, ensembles)
Implicit feedback (dwell times etc) are particularly prone to noise and biases. On the other hand, the amount of explicit data (likes, downvotes) companies can get is so small compared to implicit data, that many companies don't even bother with their explicit data. E.g. even YouTube claim not to use likes/dislikes, in order to better recommend to the long tail of items/users. So it very much does matter, being able to get good implicit data.
The interesting thing implied by OP is that TikTok seems to have completely sidestepped the pitfalls of each form of data. They in essence seem to have made implicit data be a gold standard judgment of relevance, which is really cool I think.
> I don't want to sound overly dismissive, but it's pretty clear reading this that the author has not worked on recommendation or personalization systems recently.
This is something that hasn't been done in any other large company's recommender system as far as I'm aware. It seems like a paradigm shift to me, albeit, is only possible because of the addictive nature of the app. So at least for similar UIs, this concept makes a lot of recsys research irrelevant and is highly novel. As such, I don't think you're giving OP or TikTok enough credit.
On other platforms like YouTube for example, there are a lot of factors beyond the video itself that influences if a person even clicks on it (like the thumbnail, the title, the number of views, who the creator is), so if someone doesn't click on a video, that doesn't tell you if they would have liked it or not. Because of that your data to make recommendations isn't as accurate.
That's the main difference I see with TikTok. They make recommendations proactively, instead of reactively.
(Hmm. Part of the idea presented here is that it really doesn't take that long, especially in short-form, to gather less than thirty bits worth of targeting. In a world where "bespoke" is available to everyone, will hipsters have to shift to lauding mass-production to differentiate themselves?)
> It turns out that in some categories, a machine learning algorithm significantly responsive and accurate can pierce the veil of cultural ignorance. Today, sometimes culture can be abstracted.
Which now makes me think about where algorithms are culture dependent and where not. Low level algorithms like quicksort definitely are not, but as we get "higher up" towards algorithms which work with user feedback …
Aside on this. In a 1st year CS class I was surprised to learn that half the class had one method to count the number of days in a month (30 days hath September) and the other half had another (using their knuckles). The method a person knew depended on where they grew up, and neither group had heard of the other method. So at least some search algorithms are culturally dependent :)
I tried to make an account where I only watched (in full length)/searched for/liked videos of a specific gender/race/age group.. yet even after liking over a hundred videos, then 100% of the videos they suggested to me were the wrong gender/race/age group. YouTube on the other hand is capable of recommending the correct videos after watching just a couple. Instagram’s recommendations are also far more accurate (although I dislike the fact that Instagram mostly recommend popular users).
It’s very easy (and shouldn’t take long) to conduct this experiment, just make sure that the race you target doesn’t represent the majority of the users in your proximity. For instance, if you’re in Norway then try to get TikTok to recommend Asian girls aged 20-35.. sounds like it should be easy, right?
YouTube and Instagram is easily capable of recommending whatever content you show interest in.. TikTok on the other hand feels like the reddit’s /r/popular experience which simply looks at your location and recommend a list of random stuff that people in same area have shown interest in (and completely fail to integrate your interests into this feed). I’m not saying there’s anything wrong with that, but all this hype about their algorithm makes no sense.
I mean, I heard from a friend, who certainly wasn't involved with making any of the stuff, that that's how such sites operate.
(Today I had some trouble searching for "Micro-abrasive Imperial Lapping Film" until I expanded the acronym. Doesn't Google realise I have a connector tip to polish?)
If e.g. YouTube recommended you the best items from all countries, you'd have a very bad time and would be unable to understand almost all media.
The same applies to age and gender. If you were only recommended items relevant to 90 year olds or 9 year olds, you'd have a very bad time on YouTube etc. Just because you don't notice that your media is implicitly sorted doesn't mean it isn't happening.
a few examples:
https://news.ycombinator.com/item?id=23640033 (9 year olds, RU)
https://news.ycombinator.com/item?id=24431001 (CN, IN, RU, US)
https://news.ycombinator.com/item?id=24398456 (JP, SU, US)
https://news.ycombinator.com/item?id=24156310 (caucasian variety)
Now I realise I should seek out 90-year old appropriate programming. Anyone have any hints?
Bonus clip: https://www.youtube.com/watch?v=y2fNVztaC58
Edit: After a quick calculation, I realise I have watched music from 1943: "Blood on the Risers", "Lili Marleen", "Zog Nit Keyn Mol", "Bella Ciao", and "Катюша" are all suitable for 90-year olds to have encountered as young teens.
https://www.youtube.com/watch?v=hHLRFlKPNMA (with subtitles)
and it's not as if 80s europe wasn't full of foreign-language entertainment:
https://www.youtube.com/watch?v=TDOf41Xwrc4 (caveat for US HN'ers: it's a european music video. No blood, two beasts, some breasts.)
(The 80's were big hair and shoulder pads, no matter which side of the iron curtain.)
You can't understand how age/gender/race is important for finding relevant content?! These are extremely important, fundamental, attributes that underpin good recommendations. I.e. they're so important that they generally go without saying, and are baked into the corpuses (e.g. your local news site doesn't recommend articles from another country, Disney's streaming service doesn't recommend knitting shows to children, etc)
Would you be happy if your Amazon Prime or Netflix account only showed items that are interesting to people aged {opposite age group to you} in {country on the other side of the world to you} who are {not your gender}?
I think you'd very quickly be surprised at how much media exists in the world and would quickly realise that you're mistaken.
If somebody tried to sell me a facial recognition system and it matched my face with somebody of the opposite gender and a totally different race, well, I would not buy that system.
We're talking about video/image recognition here, not politics.
My biggest gripe with it is that half of the content appears to be of the "build some tension up by hinting at a certain conclusion of the video, then simply ending the video before it comes to that conclusion and leave the viewer clueless" type, and TikToks famed algorithm seems to be entirely unable to discern that I hate this crap. Maybe it's because I watched some of these fully in the beginning until I understood the pattern. However, afterwards I swiped this kind of video away immediately once I notice the pattern, but the famed algorithm appears to be unable to re-learn that I really, really, really hate this kind of content.
People who expect a neural network to identify stuff like this have drunk WAAAAY too much ai kool-aid.
TikTok is the Kardashian effect brought down to the masses of people, to roll around in. Mimic in just the right way and you too can be Kim for 15 minutes (if your house is big enough and or you're attractive enough, as you have to be able to sell it). TikTok's most popular non-celebrities are pitching exactly the same thing that made the Kardashians so popular, in the exact same way. It's a cultural wasteland, celebrated.
What the hell is anybody going to say in 10-15 seconds that is going to be valuable outside of a quick clip of music and some status pitching (show a big house, shake your ass, take off your shirt, dive into a pool, show seven seconds of a party)? Absolutely nothing. It's the same thing Instagram is mostly used for: status positioning, signaling. There is a reason why the evidence overwhelmingly points to all of this as being unhealthy - it's all built around intentionally amplifying an addictive, desperate aspect of human nature that is a big negative at its extremes (status seeking, narcissism, bullying, social competition, selling sex, all in a swirl together and pumped up).
Is TikTok a SSCly Scissor Service?
https://slatestarcodex.com/2018/10/30/sort-by-controversial/
These videos are certainly pervasive - they are an attempt to trigger reactions to show that you like this content: for example, by getting you to watch through the end of the video, click on the creator's name for follow-up content, checking the comments for clues.
You can understand the videos as an attempt to game the algorithm, and I also think they are playing on themes which humans are genuinely addicted to and find it hard to fully control.
Long click will allow you to 'remove content from this creator' (excuse my translation): that will help.
In general, there are multiple approaches on recommender systems, to name but a few: -suggest content with similar features with what you watched -suggest content that similar users already watched -suggest completely new and/or different content
I haven't used tik tok, but you might fall into category #2 and #3. And to be honest, if i owned such an app i would pursue users to be recommended new and popular content to keep them in the platform.
1. There basically isn't content where the content consists solely of the gender/race/age of those in frame and nothing else (outside of fetishism).
2. People who strictly want to only watch that content aren't very common (outside of fetishism).
So it makes sense why your cohort might not show up on a website that has a much more intolerant stance on NSFW content than Youtube and Instagram.
If we're to believe that TikTok only recommends videos that have been human-tagged (claimed in TFA and in these comments), then it seems even more likely that a video of (your example) east asian women aged 20-35 dancing to music are tagged "dancing", (e.g.) "kpop" and not "east asian" "women" "20-35". And your feed of "dancing" + "kpop" recommendations only incidentally contain the demographic you seek while being oblivious to it.
Just guessing, of course. I have a kneejerk charity response for something when someone on HN says that it's absurdly bad. ;) If only I had this reflex at all times.
YouTube takes the content you have seen and explicitly shown that you like, and shows you a more extreme version of it. It tends to over-recommend alt-right YouTube, for example.
TikTok takes the content you have seen and implicitly shown that you like, and shows you diverse content that has some of the features you seemed to enjoy, while it learns from that.
If you are looking to fetishise young Asian women, then maybe you are on the wrong type of website (and: maybe you shouldn't do this).
Is it a bug in the algorithm, or a feature of it? If I'm a young Asian woman creator, I also have an interest in whether the algorithm is fetishising my work.
In my case the recommendations had nothing to do with what I had liked or shown interest in. It was equivalent to visiting Reddit’s /r/popular, which might appeal to most people, but from an algorithmic perspective then there’s nothing impressive about it. Like I also mentioned in another comment, I think there’s nothing wrong with this approach, I just don’t understand how people can be impressed by it from a technical point of view.
> If you are looking to fetishise young Asian women, then maybe you are on the wrong type of website (and: maybe you shouldn't do this).
I was just trying to conduct the most simple experiment imaginable to see whether it was actually capable of detecting what I showed interest in and do so in a way that I could easily verify the results. What I was looking for in the experiment represent perhaps 20% of the userbase, yet it was still incapable of displaying a single post that matched what I was trying to achieve (because the feed just show what they believe is interesting in your area). I could also have tried to achieve something more complex, like train TikTok to show skateboarding videos (which might represent less than 0.001% of their videos), but I figured I’d try with the easiest thing imaginable first (which it failed at).
And I fail to see why there’s anything wrong with being interested in watching content of people within a certain age group, or gender, or nationality..
If I were a TikTok user then I’d surely hope that they were able to recommend my videos to people that were interested in the kind of content I posted (because that would open up a lot of possibilities, like meeting people with similar interests, selling products, etc.)
Is this confirmed? That's a neat tidbit. Nice article!
No illegal content for a social media service from China is a pretty large negative point.
If they allowed illegal content, they'd have been banned even sooner. The best you can hope for is that they do not apply Chinese law outside of China, which seems to be the case, considering they have to make TikTok unavailable in China to avoid people seeing anything that's illegal in China.
The problem with "illegal" is that it is quickly extended to legally questionable. Defending content is not financially viable for any platform, even if you are in the right. In consequence it will be removed.
Twitter, Facebook and co currently attempt a regulatory capture and want to actually scrap their legal protections because they now have their censorship algorithm idiots demanded for some time.
It's also the reason why TikTok is going to be hard to scale profitably in advanced economies. Chinese staff, especially for work like moderation, is a lot cheaper.
Worth nothing that TikTok operations globally is mainly supported by Bytedance staff in China. Very skeptical on how they can achieve splitting the company and launching an IPO without moving most of those jobs moving overseas.
If they split off some staff into their own Chinese company owned by TikTok Global based in the US, it doesn't change anything.
License from Bytedance to allow TikTok the use the codebase and code also won't fly in an IPO.
The only way this makes sense is that TikTok will never IPO, this is merely a delaying tactic until the election year closes.