SVD in recommender systems
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
- University of Minnesota
- Organisation type
- Academia
- Country
- United States of America
- Published
- 14 July 2000
- Authors
- B Sarwar, G Karypis, J Konstan, J Riedl
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
- 30,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 2,126
Sources
Where this record came from and when it was last checked.
- Reference
- Application of Dimensionality Reduction in Recommender System -- A Case Study
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SVD in recommender systems was published by University of Minnesota, in United States of America, in July 2000. academia is the category the publisher falls under.
It works in Recommendation, and is recorded as doing recommender system.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Around 30,000 tokens went into training it.
Answers
SVD in recommender systems — common questions
Who created SVD in recommender systems?
SVD in recommender systems was published by University of Minnesota, based in United States of America, categorised as academia.
When was SVD in recommender systems released?
SVD in recommender systems was published in July 2000. 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 SVD in recommender systems used for?
SVD in recommender systems works in Recommendation, and is recorded as handling recommender system. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run SVD in recommender systems?
None. SVD in recommender systems 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 SVD in recommender systems open source?
The licensing for SVD in recommender systems 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 SVD in recommender systems have?
No parameter count has been published for SVD in recommender systems, 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.