# LightGCN-PyTorch

**Repository Path**: equ_cd7e/LightGCN-PyTorch

## Basic Information

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

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2021-05-24
- **Last Updated**: 2024-08-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


#### Update

2020-09:
* Change the print format of each epoch
* Add Cpp Extension in  `code/sources/`  for negative sampling. To use the extension, please install `pybind11` and `cppimport` under your environment

---

## LightGCN-pytorch

This is the Pytorch implementation for our SIGIR 2020 paper:

>SIGIR 2020. Xiangnan He, Kuan Deng ,Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang(2020). LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, [Paper in arXiv](https://arxiv.org/abs/2002.02126).

Author: Prof. Xiangnan He (staff.ustc.edu.cn/~hexn/)

(Also see Tensorflow [implementation](https://github.com/kuandeng/LightGCN))

## Introduction

In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN,including only the most essential component in GCN—neighborhood aggregation—for collaborative filtering



## Enviroment Requirement

`pip install -r requirements.txt`



## Dataset

We provide three processed datasets: Gowalla, Yelp2018 and Amazon-book and one small dataset LastFM.

see more in `dataloader.py`

## An example to run a 3-layer LightGCN

run LightGCN on **Gowalla** dataset:

* change base directory

Change `ROOT_PATH` in `code/world.py`

* command

` cd code && python main.py --decay=1e-4 --lr=0.001 --layer=3 --seed=2020 --dataset="gowalla" --topks="[20]" --recdim=64`

* log output

```shell
...
======================
EPOCH[5/1000]
BPR[sample time][16.2=15.84+0.42]
[saved][[BPR[aver loss1.128e-01]]
[0;30;43m[TEST][0m
{'precision': array([0.03315359]), 'recall': array([0.10711388]), 'ndcg': array([0.08940792])}
[TOTAL TIME] 35.9975962638855
...
======================
EPOCH[116/1000]
BPR[sample time][16.9=16.60+0.45]
[saved][[BPR[aver loss2.056e-02]]
[TOTAL TIME] 30.99874997138977
...
```

*NOTE*:

1. Even though we offer the code to split user-item matrix for matrix multiplication, we strongly suggest you don't enable it since it will extremely slow down the training speed.
2. If you feel the test process is slow, try to increase the ` testbatch` and enable `multicore`(Windows system may encounter problems with `multicore` option enabled)
3. Use `tensorboard` option, it's good.
4. Since we fix the seed(`--seed=2020` ) of `numpy` and `torch` in the beginning, if you run the command as we do above, you should have the exact output log despite the running time (check your output of *epoch 5* and *epoch 116*).


## Extend:
* If you want to run lightGCN on your own dataset, you should go to `dataloader.py`, and implement a dataloader inherited from `BasicDataset`.  Then register it in `register.py`.
* If you want to run your own models on the datasets we offer, you should go to `model.py`, and implement a model inherited from `BasicModel`.  Then register it in `register.py`.
* If you want to run your own sampling methods on the datasets and models we offer, you should go to `Procedure.py`, and implement a function. Then modify the corresponding code in `main.py`


## Results
*all metrics is under top-20*

***pytorch* version results** (stop at 1000 epochs):

(*for seed=2020*)

* gowalla:

|             | Recall | ndcg | precision |
| ----------- | ---------------------------- | ----------------- | ---- |
| **layer=1** | 0.1687               | 0.1417    | 0.05106 |
| **layer=2** | 0.1786                     | 0.1524    | 0.05456 |
| **layer=3** | 0.1824                | 0.1547 | 0.05589 |
| **layer=4** | 0.1825                 | 0.1537       | 0.05576 |

* yelp2018

|             | Recall | ndcg | precision |
| ----------- | ---------------------------- | ----------------- | ---- |
| **layer=1** | 0.05604     | 0.04557 | 0.02519 |
| **layer=2** | 0.05988               | 0.04956 | 0.0271 |
| **layer=3** | 0.06347          | 0.05238 | 0.0285 |
| **layer=4** | 0.06515                | 0.05325 | 0.02917 |