OAG-LC

This is a pytorch implementation of the OAG-LC (Order-Aware Graph Neural Network for Sequential Recommendation) and baseline methods.

Requirements

put the datasets to RNN/data/origin_data/

python main_preparedata.py

train our proposed model using main_best.py

python main_best.py

you can change the model variable in main_best.py to choose which model you want to run. GRNN is our proposed OAG-LC model.
train the baseline models using main_baseline_best.py and main_stargnn.py

python main_baseline_best.py

You can change the model variable in main_baseline_best.py to choose which baseline model you want to run. The optional variables are [GRU4Rec, NARM, SASRec, STAMP, SRGNN, GCSAN,LESSR]

python main_stargnn.py

the main_stargnn.py is used to train the SGNN-HN model

The pre-trained OAG-LC models for each dataset is located at GRNN/data/pretrained_models.

The corresponding parameters are listed as below.

数据集	learning_rate	dropout_prob	agg_layer
Electronics	0.005	0.25	1
Tmall	0.001	0	1
Movies&TV	0.005	0	1
Home&kitchen	0.005	0.25	3

The other parameters for our model are set as the default value for all the dataset, which is described in ./config/default.yaml

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
.idea		.idea
DrawImage		DrawImage
config		config
model		model
prepare_data		prepare_data
utils		utils
README.md		README.md
main.py		main.py
main_ablation_best.py		main_ablation_best.py
main_baseline.py		main_baseline.py
main_baseline_best.py		main_baseline_best.py
main_best.py		main_best.py
main_knn.py		main_knn.py
main_lessr.py		main_lessr.py
main_lessr_best.py		main_lessr_best.py
main_multi.py		main_multi.py
main_parameter_best.py		main_parameter_best.py
main_pop.py		main_pop.py
main_preparedata.py		main_preparedata.py
main_stargnn.py		main_stargnn.py
predict.py		predict.py
t_test.py		t_test.py
test.py		test.py
test2.py		test2.py