YouTube Video Recommendation System
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Organisation type
- Industry
- Country
- United States of America
- Published
- 26 September 2010
- Authors
- J Davidson, B Liebald, J Liu, P Nandy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Recommendation
- Task
- Recommender system
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"We currently handle millions of users and tens of billions of activity events with a total footprint of several terabytes of data" If 10M users each watch 1000 videos, that's 10B visualizations, which matches their "activity events" count.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited,Significant use,Historical significance
- Citations
- 1,160
Sources
Where this record came from and when it was last checked.
- Reference
- The YouTube Video Recommendation System
- Last updated
- 28 November 2025
What the numbers mean
What this model is
YouTube Video Recommendation System was published by Google, in the country recorded as United States of America, during September 2010. The publishing organisation is categorised as industry.
It works in the domain of Recommendation, and is recorded as performing the task of recommender system.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The reason it appears in this catalogue at all: highly cited,Significant use,Historical significance.
Answers
YouTube Video Recommendation System — common questions
YouTube Video Recommendation System— when was it released?
It was published in September 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
YouTube Video Recommendation System— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of recommender system. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
YouTube Video Recommendation System— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
YouTube Video Recommendation System— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
YouTube Video Recommendation System— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
YouTube Video Recommendation System— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.