LightGCN

This is our Tensorflow implementation for the 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.

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

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

Example to Run the Codes

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 [64,64,64,64] --lr 0.001 --batch_size 2048 --epoch 1000

• Yelp2018 dataset

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

• Amazon-book dataset

python LightGCN.py --dataset amazon-book --regs [1e-4] --embed_size 64 --layer_size [64,64,64,64] --lr 0.001 --batch_size 1024 --epoch 200 

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.

Improvement

parallelize sampling on CPU

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