RankBoost (EachMovie)
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
- Columbia University,Princeton University,Hebrew University of Jerusalem
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America, Israel
- Published
- 15 November 2003
- Authors
- Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Recommendation
- Task
- Recommender system, Collaborative filtering, Movie ratings
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 used the data of 61,625 viewers entering a total of 2,811,983 numeric ratings for 1,628 different movies (films and videos).
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- An Efficient Boosting Algorithm for Combining Preferences
- Last updated
- 11 February 2026
What the numbers mean
What this model is
RankBoost (EachMovie) was published by Columbia University,Princeton University,Hebrew University of Jerusalem, in United States of America, in November 2003. academia,Academia,Academia is the category the publisher falls under.
It works in Recommendation, and is recorded as doing recommender system, Collaborative filtering, Movie ratings.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
RankBoost (EachMovie) — common questions
Is RankBoost (EachMovie) open source?
No. RankBoost (EachMovie) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does RankBoost (EachMovie) have?
No parameter count has been published for RankBoost (EachMovie), which is why no memory or speed figure appears on this page.
Who created RankBoost (EachMovie)?
RankBoost (EachMovie) was published by Columbia University,Princeton University,Hebrew University of Jerusalem, based in United States of America, categorised as academia,Academia,Academia.
When was RankBoost (EachMovie) released?
RankBoost (EachMovie) was published in November 2003. 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 RankBoost (EachMovie) used for?
RankBoost (EachMovie) works in Recommendation, and is recorded as handling recommender system, Collaborative filtering, Movie ratings. 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 RankBoost (EachMovie)?
None. RankBoost (EachMovie) 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.
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.