Like Facebook before, everyone talks about the TikTok algorithm being some super secret and valuable mystery. In reality, both Facebook and TikTok succeeded because they were at the right place at the right time and didn't screw things up. The TikTok recommendation system is smart and very well implemented, but nothing novel that couldn't be implemented by a dozen other teams.
More data beats better algorithms. TikTok has vastly more interaction data by nature of its design. IG and YouTube shorts don't have nearly the volume of engaged users and are reluctant to disrupt the cash cow of their traditional interfaces.
I haven't worked on sites as big as YouTube but on sites with 100,000 members who are very much engaged with one "game" you usually find they are mainly indifferent when you offer them another "game" to play.
I like YouTube for what it is. I have interacted very little with shorts but Google has scarily seen into my imagination. I don't want to go into that rabbit hole.
The definition of "similar" is the problem w/ vector search isn't it?
Two populations can be similar in terms of conventional demographics such as age, gender, race, what kind of clothes they wear, etc. but be different in their behavior. IG users are "players of the Instagram game" and TikTok are "players of the TikTok game" and a whole system of values and behaviors are involved.
To take an example playing the "engagement farming" game on Bluesky I can follow people and know some fraction of people will follow me back, but who do I want to follow?
I postulated that the people I want are people who will repost my photos so I can try following people who repost photos but I find that reposters are not "followers" whereas I get a much better response rate if I follow people who follow another social media photographer since those people are "followers". People have an online behavior signature like that which for me matters more than the color of your skin.
Google has a ton of interaction data to be sure, but the app design decisions of TikTok (auto play, auto loop, easy swipe, easy like, etc.) extract so much more usable/actionable interaction data. The size of the like button on YouTube is a tiny percent of the screen. On TikTok the like button is the whole video.
Not just that. The whole UI is designed for behavioral data aggregation.
It’s not just “did you click the like button”. It’s “did you swipe it away? How long did you watch until you swiped it away? Did you come back afterwards? Did you let it loop multiple times before moving on?”.
They’ll capture likes and dislikes you yourself probably didn’t even knew you had, just from tens and hundreds of these micro actions. And they’ll do it in the very first hour of you using the app, whereas YouTube won’t know too much about you even after months of you using it.
A TikTok user may watch hundreds of videos and like dozens of them in a single viewing session. A YouTube user might watch ... 4? YouTube tried to force 10+ minute videos so they could insert television-style commercials.
IG reels and YouTube shorts are crap because creators create good content only for the place where all the audience is, which is TikTok. When users open TikTok they expect TikTok-style content. When users open YouTube they don't expect TikTok-style content, in fact they hate it. Same with IG reels.
It has nothing to do with the quality of the algorithm. In fact the YT algorithm has gotten worse since they introduced shorts because they shove shorts into people's faces.
A better question would be why is the regular YouTube algorithm so bad. And the answer is because it doesn't optimize at all for the consumers, but for the producers (producers of ads, that is). TT has figured out it doesn't matter what people consume as long as they consume, whereas YT is bullish into controlling what people consume.
My take: it's a mix of brand bundling and lack of data. They're roughly equivalent but shorts is bundled with youtube which has its own brand perception and reels are bundled with IG/FB and have their own brand perception. Additionally fewer users means less algorithmic data to keep viewers.
Tiktok was allowed to establish its own brand and develop a community while shorts and reels are intrinsically tied to their past. They may be able to escape that history but I don't think it's helping them be fast movers or win "cool" points.
> My take: it's a mix of brand bundling and lack of data. They're roughly equivalent but shorts is bundled with youtube which has its own brand perception and reels are bundled with IG/FB and have their own brand perception. Additionally fewer users means less algorithmic data to keep viewers.
My intuition would work the other way around. I'd expect offerings from more established companies to have a big leg up in terms of usable data. Youtube should be able to use a viewer's entire watch/subscription history to inform itself about what shorts a user might like, even before they've interacted with their first short. Bytedance, on the other hand, has to start from scratch with each truly new user.
The coolness or stodginess of the company would be secondary to its effects. If boring-old-Youtube could promise shorts creators great exposure to an enthusiastic audience, it would win the platform regardless of its brand.
I'll argue that TikTok's structure which offers you one video at a time gives you much more useful information than YouTube's interface, which looks like
TikTok gets a definite thumbs up or thumbs down for every video it shows you whereas if you click on one particular sidebar video YouTube can make no conclusion about how you felt about the other videos in the sidebar. The recommendation literature talks about "negative sampling" to overcome this, I never could really believe in it, I think now it doesn't really work.
I built a system like that and found that, paradoxically, you have to make it blend in a good amount of content that it doesn't think you'd like for it to be able to calibrate itself.
> If boring-old-Youtube could promise shorts creators great exposure to an enthusiastic audience, it would win the platform regardless of its brand.
Just a guess, as someone who makes their living from YouTube: YouTube creators are driven to create content that earns them money. As compared to long-form content, YouTube shorts earn next-to-nothing, and it’s not clear that they drive significant new traffic to more-valuable content.
Most large creators on YouTube are focused on the bottom line, not exposure.
The reason shorts don't earn any money, as compared to Instagram and TikTok, is that they don't advertise crap for me to buy (I have YT premium), so I don't end up buying shit there like I do the other two.
Having read the paper, what's unique about Bytedance's approach is how relatively simple it is at its core - obviously there's a lot of complexity around it to do it at scale, but I feel like it's simpler than the social-graph based approaches.
The features used by their algorithm tells you what a user is interested, historically.
Contrast this to Meta, which uses the social graph as their features. Imagine features like the number of times a user likes another author's / cluster's content.
Tiktok will serve you $TOPIC because you have $INTERACTED with $TOPIC historically.
Meta will serve you $TOPIC because you have $INTERACTED with $PEOPLE who post $TOPIC, historically.
It's because they originally built their recommendation system to recommend friends and their content. Here, the social graph makes complete sense as the foundation for their simple search algorithm.
But as they expanded their recommendation capabilities, the features stuck around. It's the same reason why tech debt accumulates. Data sticks around in the same way code does. But data is even higher friction, since it's a superset of the code.
Everyone talks about TikTok having some valuable algorithm, but I keep running into the most dimwitted nonsense every time I try it.
Then again, perhaps this is the algorithm's way of scaring me away to save precious bandwidth, knowing full well that I will never buy products from online ads anyway?
Additional evidence that TikTok doesn't possess special algorithm and infrastructure prowess is the complete failure(/gaming?) of search. Many (most?!) searches on TikTok have been returning irrelevant shock and/or porn content for weeks.
> What did Livejournal, Xanalga, Myspace, Tumblr, all screw up
Don't forget Friendster!
Mainly bad-luck in being too early or too late, though MySpace had some self-inflicted problems with performance and overcomplicating the interface (by allowing customization).
This just feels like special pleasing. Why did Facebook get the timing exactly right and what was different about that time vs. a better understanding of user wants, well planned rollout and promotion strategy that created desire to have it, and not being too greedy.
