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ByteDance's Recommendation System

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Re: ByteDance's Recommendation System

#31

Post by mrfox321 »

PaulHoule wrote: post #23
mrfox321 wrote: post #17

Earlier quoted context omitted.

IG has plenty of data (I did ML at IG). Don't be naive.

It is the users and not the company. 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.

IG users and Tiktok users were / are quite similar. Especially when Tiktok wasn't yet eating IGs lunch.

Re: ByteDance's Recommendation System

#32

Post by mrfox321 »

spamizbad wrote: post #30
mrfox321 wrote: post #9

Earlier quoted context omitted.

Not true. Why do IG reels and YouTube shorts suck, then? They clearly built something superior. And it can't seem to be matched by the biggest tech companies.

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.

It's simpler intuition but more complex from a data / ml perspective.

Their algorithm is really built around their features. Specifically, temporal representations of user interest:

https://ieeexplore.ieee.org/document/9458799/

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.

Meta only coincidentally gives you what you like.

Tiktok knows what you like.

This is the difference. This is why IG is losing.

Re: ByteDance's Recommendation System

#33

Post by paulluuk »

tasn wrote: post #21

I very much doubt this is the TikTok algorithm. 1. It doesn't even claim to be that. 2. It's over a year old - so even if it was, this is no longer it. 3. There's zero incentive for them to release it, but every incentive to release a fake one. 4. TikTok is too much of a national asset, I doubt the Chinese government would not use it to their advantage (and transparency would be counter to that). Edit: there's also h…

From what I can tell, they didn't release the actual model, but rather a kind of framework for running and training models in a distributed way. The only actual "model" in there appears to be three Dense layers in a "demo" file.

Re: ByteDance's Recommendation System

#34

Post by xnx »

mrfox321 wrote: post #32
spamizbad wrote: post #30

Earlier quoted context omitted.

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.

It's simpler intuition but more complex from a data / ml perspective. Their algorithm is really built around their features. Specifically, temporal representations of user interest: https://ieeexplore.ieee.org/document/9458799/ 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 o…

That's a crazy design choice by meta if true. The interests of those in my social graph have very little connection to my interests.

Re: ByteDance's Recommendation System

#35

Post by mrfox321 »

tasn wrote: post #21

I very much doubt this is the TikTok algorithm. 1. It doesn't even claim to be that. 2. It's over a year old - so even if it was, this is no longer it. 3. There's zero incentive for them to release it, but every incentive to release a fake one. 4. TikTok is too much of a national asset, I doubt the Chinese government would not use it to their advantage (and transparency would be counter to that). Edit: there's also h…

I mean, it's their serving infra, but not their features. See my reply in another parent comment.

Re: ByteDance's Recommendation System

#36

Post by [deleted] »

mrfox321 wrote: post #32
spamizbad wrote: post #30

Earlier quoted context omitted.

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.

It's simpler intuition but more complex from a data / ml perspective. Their algorithm is really built around their features. Specifically, temporal representations of user interest: https://ieeexplore.ieee.org/document/9458799/ 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 o…

[deleted]

Re: ByteDance's Recommendation System

#37

Post by xnx »

wiseowise wrote: post #19
xnx wrote: post #12

Earlier quoted context omitted.

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.

More data? Seriously? What has more data than YouTube?

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.

Re: ByteDance's Recommendation System

#38

Post by xnx »

newsclues wrote: post #15
xnx wrote: post #12

Earlier quoted context omitted.

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 question the assertion that TikTok has more interaction data than Google.

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.

Re: ByteDance's Recommendation System

#40

Post by voxic11 »

tw1984 wrote: post #20

for those interested, Chinese laws forbid the export of recommendation systems, unless ByteDance is challenging the Chinese laws here, which is highly unlikely, this simple can not be the recommendation system used in their production. will be far more interesting to know say what is the difference, what got changed/removed to make them feel comfortable that such an open source variant won't get them into troubles wi…

This is essentially the framework for executing their recommendation system but the actual piece which determines the recommendation is a model called "demo" so I presume its not the actual ML model they use in production.
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