Comparisons / Neural Collaborative Filtering vs NeuMF (Fused GMF + MLP)
Neural Collaborative Filtering vs NeuMF (Fused GMF + MLP)
Neural collaborative filtering against its generalised-matrix-factorisation hybrid.
NeuMF (Fused GMF + MLP) has 70M more parameters than Neural Collaborative Filtering: 4 layers added, 5 changed.
Neural Collaborative Filtering
- Layers
- 9
- Parameters
- 35M
- Input
- 1
- Output
- 1 × 1
- Forward-passes
- yes
- Est. train cost
- $0.053
NeuMF (Fused GMF + MLP)
- Layers
- 13
- Parameters
- 106M
- Input
- 1
- Output
- 1 × 1
- Forward-passes
- yes
- Est. train cost
- $0.076
The deltas
Every number is NeuMF (Fused GMF + MLP) relative to Neural Collaborative Filtering.
Which GPUs each one fits
Memory for the graph at the input shape both declare. A highlighted row is a card one of them fits and the other does not, which is the difference that decides a purchase.
| GPU | Neural Collaborative Filtering | NeuMF (Fused GMF + MLP) |
|---|---|---|
| T4 16GB | fits | fits |
| A100 40GB | fits | fits |
| H100 80GB | fits | fits |
Layer by layer
Aligned in topological order. 7 of 16 rows are the same layer with the same parameters.
Hide all 16 rows
| Neural Collaborative Filtering | NeuMF (Fused GMF + MLP) | ||||||
|---|---|---|---|---|---|---|---|
| Layer | Params | Output | Layer | Params | Output | ||
| 1 | same | User ID Input | 1 | User ID Input | 1 | ||
| 2 | same | Item ID Input | 1 | Item ID Input | 1 | ||
| 3 | added | — | User MF Emb Embedding | 3.2M | 1 × 32 | ||
| 4 | added | — | User MLP Emb Embedding | 6.4M | 1 × 64 | ||
| 5 | changed vocabSize | User Embedding Embedding | 3.2M | 1 × 32 | Item MF Emb Embedding | 32M | 1 × 32 |
| 6 | changed embeddingDim | Item Embedding Embedding | 32M | 1 × 32 | Item MLP Emb Embedding | 64M | 1 × 64 |
| 7 | added | — | GMF (⊙) Multiply | 1 × 32 | |||
| 8 | changed dim, numInputs, axis | Concat [u; i] Concatenate | 1 × 64 | Concat Concatenate | 1 × 128 | ||
| 9 | changed inFeatures | MLP 1 Linear | 4.2K | 1 × 64 | MLP FC 1 Linear | 8.3K | 1 × 64 |
| 10–12 | same | 3 unchanged layers | |||||
| 13 | added | — | Fuse GMF+MLP Concatenate | 1 × 64 | |||
| 14 | changed inFeatures | Score Head Linear | 33 | 1 × 1 | Predict Linear | 65 | 1 × 1 |
| 15 | same | Sigmoid Sigmoid | 1 × 1 | Sigmoid Sigmoid | 1 × 1 | ||
| 16 | same | P(click) Output | 1 × 1 | P(rating) Output | 1 × 1 | ||
What this is not
- Parameter counts are derived from the graph, not read from a checkpoint. They are exact for a graph that is fully specified and approximate for one that is not.
- Cost and GPU fit are estimates from the graph under one set of assumptions, not measurements of a run.
Take it further
Open either graph in the editor, change it, and check it again: Neural Collaborative Filtering · NeuMF (Fused GMF + MLP)
Compare any two models of your own, including anything on Hugging Face: the comparison tool.
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POST https://www.neurarch.com/api/v1/plan for a graph of your own.