Regularized SVD for Collaborative Filtering
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
- Warsaw University
- Organisation type
- Academia
- Country
- Poland
- Published
- 12 August 2007
- Authors
- A Paterek
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.
- Citations
- 1,117
Sources
Where this record came from and when it was last checked.
- Reference
- Improving regularized singular value decomposition for collaborative filtering
- Last updated
- 28 November 2025
What the numbers mean
Background
Regularized SVD for Collaborative Filtering was published by Warsaw University, in Poland, in August 2007. The organisation is categorised as academia.
It works in Recommendation, and is recorded as doing recommender system.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training set ran to roughly 100,480,507 tokens.
Answers
Regularized SVD for Collaborative Filtering — common questions
Who created Regularized SVD for Collaborative Filtering?
Regularized SVD for Collaborative Filtering was published by Warsaw University, based in Poland, categorised as academia.
When was Regularized SVD for Collaborative Filtering released?
Regularized SVD for Collaborative Filtering was published in August 2007. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Regularized SVD for Collaborative Filtering used for?
Regularized SVD for Collaborative Filtering works in Recommendation, and is recorded as handling 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.
What GPU do I need to run Regularized SVD for Collaborative Filtering?
None. Regularized SVD for Collaborative Filtering 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.
Is Regularized SVD for Collaborative Filtering open source?
The licensing for Regularized SVD for Collaborative Filtering was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Regularized SVD for Collaborative Filtering have?
No parameter count has been published for Regularized SVD for Collaborative Filtering, which is why no memory or speed figure appears on this page.
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.