# NGCF-PyTorch

**Repository Path**: cpai/NGCF-PyTorch

## Basic Information

- **Project Name**: NGCF-PyTorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-04-11
- **Last Updated**: 2021-11-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Neural Graph Collaborative Filtering
This is my PyTorch implementation for the paper:

>Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua (2019). Neural Graph Collaborative Filtering, [Paper in ACM DL](https://dl.acm.org/citation.cfm?doid=3331184.3331267) or [Paper in arXiv](https://arxiv.org/abs/1905.08108). In SIGIR'19, Paris, France, July 21-25, 2019.

The TensorFlow implementation can be found [here](<https://github.com/xiangwang1223/neural_graph_collaborative_filtering>).

## Introduction
My implementation mainly refers to the original TensorFlow implementation. It has the evaluation metrics as the original project. Here is the example of Gowalla dataset:

```
Best Iter=[38]@[32904.5]	recall=[0.15571	0.21793	0.26385	0.30103	0.33170], precision=[0.04763	0.03370	0.02744	0.02359	0.02088], hit=[0.53996	0.64559	0.70464	0.74546	0.77406], ndcg=[0.22752	0.26555	0.29044	0.30926	0.32406]
```

Hope it can help you!

## Environment Requirement
The code has been tested under Python 3.6.9. The required packages are as follows:
* pytorch == 1.3.1
* numpy == 1.18.1
* scipy == 1.3.2
* sklearn == 0.21.3

## Example to Run the Codes
The instruction of commands has been clearly stated in the codes (see the parser function in NGCF/utility/parser.py).
* Gowalla dataset
```
python main.py --dataset gowalla --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0001 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 400 --verbose 1 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1]
```

* Amazon-book dataset
```
python main.py --dataset amazon-book --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0005 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 200 --verbose 50 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1]
```
## Supplement

* The parameter `negative_slope` of LeakyReLu was set to 0.2, since the default value of PyTorch and TensorFlow is different.
* If the arguement `node_dropout_flag` is set to 1, it will lead to higher calculational cost.