RankBoost (meta-search)
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, Search
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
16 (ML) and 22 (UNIV) query-expansion templates; retrieve top-30 URLs per template; combine via RankBoost. ML: 210 base queries; UNIV: 290.
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 (meta-search) was published by Columbia University,Princeton University,Hebrew University of Jerusalem, in the country recorded as United States of America, during November 2003. The publishing organisation is categorised as academia,Academia,Academia.
It works in the domain of Recommendation, and is recorded as performing the task of recommender system, Search.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
RankBoost (meta-search) — common questions
RankBoost (meta-search)— who created it?
It was published by Columbia University,Princeton University,Hebrew University of Jerusalem, based in United States of America, an organisation categorised as academia,Academia,Academia.
RankBoost (meta-search)— when was it released?
It 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.
RankBoost (meta-search)— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of recommender system, Search. These are the areas it was designed around; they describe intent rather than a hard boundary.
RankBoost (meta-search)— 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.
RankBoost (meta-search)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RankBoost (meta-search)— 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.
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.