MatrixFac for Recommenders
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
- Yahoo Research,AT&T
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 7 August 2009
- Authors
- Yehuda Koren, Robert Bell, and Chris Volinsky
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
- 100,480,507 tokens
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
- Citations
- 9,234
Sources
Where this record came from and when it was last checked.
- Reference
- Matrix factorization techniques for recommender systems
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
MatrixFac for Recommenders was published by Yahoo Research,AT&T, in the country recorded as United States of America, during August 2009. It comes out of an organisation categorised as industry,Industry.
It works in the domain of Recommendation, and is recorded as performing the task of recommender system.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training consumed a corpus of around 100,480,507 tokens of text.
Its inclusion criterion: highly cited.
Answers
MatrixFac for Recommenders — common questions
MatrixFac for Recommenders— 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.
MatrixFac for Recommenders— who created it?
It was published by Yahoo Research,AT&T, based in United States of America, an organisation categorised as industry,Industry.
MatrixFac for Recommenders— when was it released?
It was published in August 2009. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
MatrixFac for Recommenders— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of recommender system. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MatrixFac for Recommenders— 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.
MatrixFac for Recommenders— 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.
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.