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LightGCN

This is our Tensorflow implementation for our SIGIR 2020 paper:

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.

Contributors: Dr. Xiangnan He (staff.ustc.edu.cn/~hexn/), Kuan Deng, Yingxin Wu.

(We also provide Pytorch implementation for LightGCN : https://github.com/gusye1234/LightGCN-PyTorch. Contributors: Jianbai Ye.)

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.

Environment Requirement

The code has been tested running under Python 3.6.5. The required packages are as follows:

C++ evaluator

We have implemented C++ code to output metrics during and after training, which is much more efficient than python evaluator. It needs to be compiled first using the following command.

python setup.py build_ext --inplace

After compilation, the C++ code will run by default instead of Python code.

Examples to run a 3-layer LightGCN

The instruction of commands has been clearly stated in the codes (see the parser function in LightGCN/utility/parser.py).

Gowalla dataset

python LightGCN.py --dataset gowalla --regs [1e-4] --embed_size 64 --layer_size [64,64,64] --lr 0.001 --batch_size 2048 --epoch 1000

eval_score_matrix_foldout with cpp
n_users=29858, n_items=40981
n_interactions=1027370
n_train=810128, n_test=217242, sparsity=0.00084
      ...
Epoch 1 [30.3s]: train==[0.46925=0.46911 + 0.00014]
Epoch 2 [27.1s]: train==[0.21866=0.21817 + 0.00048]
      ...
Epoch 879 [81.6s + 31.3s]: test==[0.13271=0.12645 + 0.00626 + 0.00000], recall=[0.18201], precision=[0.05601], ndcg=[0.15555]
Early stopping is trigger at step: 5 log:0.18201370537281036
Best Iter=[38]@[32829.6]	recall=[0.18236], precision=[0.05607], ndcg=[0.15539]

Yelp2018 dataset

python LightGCN.py --dataset yelp2018 --regs [1e-4] --embed_size 64 --layer_size [64,64,64] --lr 0.001 --batch_size 2048 --epoch 1000

eval_score_matrix_foldout with cpp
n_users=31668, n_items=38048
n_interactions=1561406
n_train=1237259, n_test=324147, sparsity=0.00130
    ...
Epoch 1 [56.5s]: train==[0.33843=0.33815 + 0.00028]
Epoch 2 [53.1s]: train==[0.16253=0.16192 + 0.00061]
    ...
Epoch 679 [104.6s + 12.9s]: test==[0.17217=0.16289 + 0.00929 + 0.00000], recall=[0.06359], precision=[0.02874], ndcg=[0.05240]
Early stopping is trigger at step: 5 log:0.06359195709228516
Best Iter=[28]@[42815.0]	recall=[0.06367], precision=[0.02868], ndcg=[0.05236]

Amazon-book dataset

python LightGCN.py --dataset amazon-book --regs [1e-4] --embed_size 64 --layer_size [64,64,64] --lr 0.001 --batch_size 8192 --epoch 1000

eval_score_matrix_foldout with cpp
n_users=52643, n_items=91599
n_interactions=2984108
n_train=2380730, n_test=603378, sparsity=0.00062
    ...
Epoch 1 [53.2s]: train==[0.57471=0.57463 + 0.00008]
Epoch 2 [47.3s]: train==[0.31518=0.31478 + 0.00040]
    ...
Epoch 779 [181.7s + 79.0s]: test==[0.20300=0.19434 + 0.00866 + 0.00000], recall=[0.04120], precision=[0.01703], ndcg=[0.03186]
Early stopping is trigger at step: 5 log:0.04119725897908211
Best Iter=[33]@[49875.4]	recall=[0.04123], precision=[0.01710], ndcg=[0.03189]

NOTE : the duration of training and testing depends on the running environment.

Dataset

We provide three processed datasets: Gowalla, Yelp2018 and Amazon-book.

Efficiency Improvements:

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