guoyang9 / Ncf
A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17
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NCF
A pytorch GPU implementation of He et al. "Neural Collaborative Filtering" at WWW'17
Note that I use the two sub datasets provided by Xiangnan's repo.
I randomly utilized a factor number 32, MLP layers 3, epochs is 20, and posted the results in the original paper and this implementation here. I employed the exactly same settings with Xiangnan, including batch_size, learning rate, and all the initialization methods in Xiangnan's keras repo. From the results I observed, this repo can replicate the performance of the original NCF. Xiangnan's keras repo:
| Models | MovieLens [email protected] | MovieLens [email protected] | Pinterest [email protected] | Pinterest [email protected] |
|---|---|---|---|---|
| MLP | 0.692 | 0.425 | 0.868 | 0.542 |
| GMF | - | - | - | - |
| NeuMF (without pre-training) | 0.701 | 0.425 | 0.870 | 0.549 |
| NeuMF (with pre-training) | 0.726 | 0.445 | 0.879 | 0.555 |
This pytorch code:
| Models | MovieLens [email protected] | MovieLens [email protected] | Pinterest [email protected] | Pinterest [email protected] |
|---|---|---|---|---|
| MLP | 0.691 | 0.416 | 0.866 | 0.537 |
| GMF | 0.708 | 0.429 | 0.867 | 0.546 |
| NeuMF (without pre-training) | 0.701 | 0.424 | 0.867 | 0.544 |
| NeuMF (with pre-training) | 0.720 | 0.439 | 0.879 | 0.555 |
The requirements are as follows:
* python==3.6
* pandas==0.24.2
* numpy==1.16.2
* pytorch==1.0.1
* gensim==3.7.1
* tensorboardX==1.6 (mainly useful when you want to visulize the loss, see https://github.com/lanpa/tensorboard-pytorch)
Example to run:
python main.py --batch_size=256 --lr=0.001 --factor_num=16
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