tmukande-debug / CTGNN

Multi-dimentional Information Integrated Graph Neural Network for Sequential Recommendation

CTGNN

Multi-dimentional Information Integrated Graph Neural Network for Sequential Recommendation

We setup our experiment on a Titan V and 256G memory on CentOs.

Experiment Environment

• python 3.6.5
• tensorflow-gpu 1.12
• numpy

Project Struct

datasets file

• data\ctgnn\adj_matrix\ctgnn_clothes\s_norm_adj_mat_time.npz ------ the adjacent matrix of Amazon clothes dataset
• data\ctgnn\ctgnn_clothes_category_idx.pk ------ item categories of Amazon clothes
• data\ctgnn\ctgnn_clothes_time_4.txt.pk ------ train and test dataset

program file

• main_ctgnn.py ------ this is the program entry
• model_ctgnn.py ------ the CTGNN model
• utils.py ------ some helper functions we used
• modules_time.py ------ the CTGNN model used function
• sampler_time_gcn.py ------ the data processing function

Recommended Setup

You can run the main_ctgnn.py directly for easily running the program. If you run the code on linux, just running the following command:

python main_ctgnn.py