NeuMF (Pinterest)
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
- Shandong University,Texas A&M,National University of Singapore,Columbia University
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- China, United States of America, Singapore
- Published
- 16 August 2017
- Authors
- X He, L Liao, H Zhang, L Nie, X Hu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Recommendation
- Task
- Collaborative filtering
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
- 1,500,809 tokens
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
- Record confidence
- Unknown
- Citations
- 7,004
Sources
Where this record came from and when it was last checked.
- Reference
- Neural Collaborative Filtering
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
NeuMF (Pinterest) was published by Shandong University,Texas A&M,National University of Singapore,Columbia University, in the country recorded as China, during August 2017. The category the publisher falls under is academia,Academia,Academia,Academia.
It works in the domain of Recommendation, and is recorded as performing the task of collaborative filtering.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 1,500,809 tokens of text.
Its inclusion criterion: highly cited.
Answers
NeuMF (Pinterest) — common questions
NeuMF (Pinterest)— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
NeuMF (Pinterest)— 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.
NeuMF (Pinterest)— who created it?
It was published by Shandong University,Texas A&M,National University of Singapore,Columbia University, based in China, an organisation categorised as academia,Academia,Academia,Academia.
NeuMF (Pinterest)— when was it released?
It was published in August 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
NeuMF (Pinterest)— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of collaborative filtering. These are the areas it was designed around; they describe intent rather than a hard boundary.
NeuMF (Pinterest)— 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.
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.