This is the code for the ICDM 2020 Paper: Dynamic Graph Collaborative Filtering.
Our code mainly mainly refers to jodie: https://github.com/srijankr/jodie/, data download method, running environment and initialization are the same as jodie.
To train the DGCF model using the data/<network>.csv dataset, use the following command. This will save a model for every epoch in the saved_models/<network>/ directory.
python DGCF.py --network <network> --model DGCF --epochs 50 --method attention --adj
This code can be given the following command-line arguments:
--network: choose to the train data:reddit\wikipedia\lastfm
--model: this is the name of the model
--epochs: this is the maximum number of interactions to train the model.
--embedding_dim: this is the number of dimensions of the dynamic embedding.
--method: this is the type of aggregator function in second-order aggregation
--adj: this is a boolean input indicating if use the second update.
--length： this is the aggregator size in second-order aggegator function.
To evaluate the performance of the interaction prediction task in one epoch, use the following command:
python evaluate_interaction_prediction.py --network --model --method
To evaluate the performance of the interaction prediction task in all epoch, user the followinig command:
python evaluate_all.py --network --model --method
For detailed code execution, you can refer to the command line in the ./Shell folder.
- python3
- jsonlines
- pytorch
- torch-geometric
Please cite our paper if you use the code:
@inproceedings{li2020dynamic,
title={Dynamic graph collaborative filtering},
author={Li, Xiaohan and Zhang, Mengqi and Wu, Shu and Liu, Zheng and Wang, Liang and Philip, S Yu},
booktitle={2020 IEEE International Conference on Data Mining (ICDM)},
pages={322--331},
year={2020},
organization={IEEE}
}