IA-GCN: Interactive Graph Convolutional Network for Recommendation

Overview

This is our Tensorflow implementation for our CIKM 2023 short paper:

Zhang, Yinan, et al. "BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation." Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023. (https://dl.acm.org/doi/abs/10.1145/3583780.3615232).

We also provide a long version on arxiv: IA-GCN: Interactive Graph Convolutional Network for Recommendation (https://arxiv.org/abs/2204.03827).

Introduction

In this work, we propose a novel graph attention model named Interactive GCN (IA-GCN), which introduces bilateral interactive guidance into each user-item pair for preference prediction. By this manner, we can obtain target-aware representations, i.e., the information of the target item/user is explicitly encoded in the user/item representation, for more precise matching.

Requirements

The required packages are as follows:

• numpy (1.15.0)
• tensorflow (1.12.0)

Quick Start

cd electronics/l2_hyper_weights
python -u /export/App/training_platform/PinoModel/Light_GCN_ops.py --dataset=electronics --regs=[1e-4] --embed_size=64 --layer_size=[64,64] --lr=6.5e-05 --batch_size=1024 --epoch=1000

Example to run 2-layer IA-GCN

• For data preprocessing, run make_pkl function located in electronics/l2_hyper_weights/utility/load_data.py to generate 'data_bin'. Note: the parameter layer_num equals 2 in this example but needs to be changed accordingly.
• for custom op compliation, run following commands to generate the 'tree_out_load_more.so' file, and put it in the main workspace.

  cd ops/l2
  sh 1.build.sh
  

• To Train a model, run the following command

  python -u /export/App/training_platform/PinoModel/Light_GCN_ops.py --dataset=electronics --regs=[1e-4] --embed_size=64 --layer_size=[64,64] --lr=6.5e-05 --batch_size=1024 --epoch=1000
  

Dataset

We use four open datasets: Amazon-Electronics, Gowalla, Yelp2018, Amazon-Book, which vary in domains, scale, and density. We closely follow the same data split strategy as existing GCN- based CF works [1, 2]

Dataset	#Users	#Items	#Interactions	Density
Amazon-Electronics	1435	1522	35931	0.01654
Gowalla	29528	40981	1027370	0.00084
Yelp2018	31668	38048	1561406	0.00130
Amazon-Book	52643	91599	2984108	0.00062

References

[1] He, Xiangnan, et al. "Lightgcn: Simplifying and powering graph convolution network for recommendation." Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 2020.

[2] Mao, Kelong, et al. "UltraGCN: ultra simplification of graph convolutional networks for recommendation." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021.