witheunjin/ncf-guoyang

A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17

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 HR@10	MovieLens NDCG@10	Pinterest HR@10	Pinterest NDCG@10
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 HR@10	MovieLens NDCG@10	Pinterest HR@10	Pinterest NDCG@10
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