GroupLens
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
- Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 22 October 1994
- Authors
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John 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
- tokens
For each pair of users, the system computes the correlation between their scores in the articles they have rated. Then to make the prediction of a score for a given article and user the system computes a weighted average taking into account the correlations with each other user, the average rating of each user and the average rating of the article. So the system in total has n+m+n*n ~= n*n parameters, where n is the number of users and m is the number of articles. To address scaling issues, t…
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
- 7,733
Sources
Where this record came from and when it was last checked.
- Reference
- GroupLens: an Open Architecture for Collaborative Filtering of Netnews
- Last updated
- 28 November 2025
What the numbers mean
Background
GroupLens was published by Massachusetts Institute of Technology (MIT), in United States of America, in October 1994. It comes out of academia.
It works in Recommendation, and is recorded as doing 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
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
GroupLens — common questions
Who created GroupLens?
GroupLens was published by Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia.
When was GroupLens released?
GroupLens was published in October 1994. 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 GroupLens used for?
GroupLens works in Recommendation, and is recorded as handling recommender system. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run GroupLens?
None. GroupLens 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 GroupLens open source?
The licensing for GroupLens 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 GroupLens have?
No parameter count has been published for GroupLens, 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.