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.

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

(Also see Tensorflow implementation)

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

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

...
======================
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).

Update

1. Change the print format of each epoch
2. Add Cpp Extension in

   code/sources/

   for negative sampling. To use the extension, please install

   pybind11

   and

   cppimport

   under your environment

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

tensorflow version results:

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 | 0.4557 | 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 |