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.

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:

• tensorflow == 1.11.0
• numpy == 1.14.3
• scipy == 1.1.0
• sklearn == 0.19.1
• cython == 0.29.15

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

• Command

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

• Output log :

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 1000 [51.4s + 11.1s]: test==[0.13277=0.12649 + 0.00627 + 0.00000], recall=[0.18123], precision=[0.05618], ndcg=[0.15553]
Best Iter=[45]@[28563.4]	recall=[0.18198], precision=[0.05613], ndcg=[0.15523]

Yelp2018 dataset

• Command

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

• Output log :

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 840 [91.6s + 10.5s]: test==[0.17441=0.16502 + 0.00939 + 0.00000], recall=[0.06398], precision=[0.02880], ndcg=[0.05265]
Early stopping is trigger at step: 5 log:0.06398
Best Iter=[36]@[47231.3]	recall=[0.06446], precision=[0.02890], ndcg=[0.05279]

Amazon-book dataset

• Command

python LightGCN.py --dataset amazon-book --regs [1e-4] --embed_size 64 --layer_size [64,64,64] --lr 0.001 --batch_size 4096 --epoch 1000

• Output log :

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 1000 [123.4s + 20.6s]: test==[0.20296=0.19421 + 0.00875 + 0.00000], recall=[0.04171], precision=[0.01725], ndcg=[0.03224]
Best Iter=[49]@[51723.6]	recall=[0.04171], precision=[0.01725], ndcg=[0.03224]

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

Dataset

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

• train.txt

  • Train file.
  • Each line is a user with her/his positive interactions with items: userID\t a list of itemID\n.

• test.txt

  • Test file (positive instances).
  • Each line is a user with her/his positive interactions with items: userID\t a list of itemID\n.
  • Note that here we treat all unobserved interactions as the negative instances when reporting performance.

• user_list.txt

  • User file.
  • Each line is a triplet (org_id, remap_id) for one user, where org_id and remap_id represent the ID of the user in the original and our datasets, respectively.

• item_list.txt

  • Item file.
  • Each line is a triplet (org_id, remap_id) for one item, where org_id and remap_id represent the ID of the item in the original and our datasets, respectively.

Efficiency Improvements:

• Parallelized sampling on CPU
• C++ evaluation for top-k recommendation

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