https://github.com/CRIPAC-DIG/DGCF

[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."
https://github.com/CRIPAC-DIG/DGCF

collaborative-filtering dynamic-graphs recommender-system

Last synced: 3 months ago
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[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."

• Host: GitHub
• URL: https://github.com/CRIPAC-DIG/DGCF
• Owner: CRIPAC-DIG
• Created: 2020-09-22T01:18:57.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2024-04-24T09:16:23.000Z (7 months ago)
• Last Synced: 2024-06-24T05:53:55.007Z (5 months ago)
• Topics: collaborative-filtering, dynamic-graphs, recommender-system
• Language: Python
• Homepage:
• Size: 334 KB
• Stars: 40
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DGCF



This is the code for the ICDM 2020 Paper: [Dynamic Graph Collaborative Filtering](https://ieeexplore.ieee.org/abstract/document/9338436).

## Usage

Our code mainly mainly refers to jodie: https://github.com/srijankr/jodie/, data download method, running environment and initialization are the same as jodie.

### Train model

To train the DGCF model using the ```data/.csv``` dataset, use the following command. This will save a model for every epoch in the ```saved_models//``` directory.

```python DGCF.py --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.

### Evaluate the model

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.

## Requirements

- python3

- jsonlines

- pytorch

- torch-geometric

## Citation

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}

}

```