Multi-dimentional Information Integrated Graph Neural Network for Sequential Recommendation
We setup our experiment on a Titan V and 256G memory on CentOs.
- python 3.6.5
- tensorflow-gpu 1.12
- numpy
- 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
- 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
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