xtwentian3 / lcfn Goto Github PK

Codes for papers:

1. Wenhui Yu and Zheng Qin. 2020. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. In ICML.

2. Wenhui Yu, Zixin Zhang, Junfeng Ge, Wenwu Ou, and Zheng Qin. 2021. Less is More: Exploring Simple and Powerful Low-pass Graph Convolutional Network for Recommendation.

3. Wenhui Yu, Xiao Lin, Jinfei Liu, Junfeng Ge, Wenwu Ou, and Zheng Qin. 2021. Self-propagation Graph Neural Network for Recommendation. In TKDE.

This project is for our model LCFN and baselines.

* Environment:
  Python 3.6.8 :: Anaconda, Inc.
* Libraries:
  tensorflow 1.12.0
  numpy 1.16.4
  scipy 0.18.1
  pandas 0.18.1
  openpyxl 2.3.2
  xlrd 1.0.0
  xlutils 2.0.0

Please follow the steps below:
1. Pretraining
    1.1 Run _hypergraph_embeddings.py in folder pretraining.
    1.2 Run _graph_embeddings.py in folder pretraining.
    1.3 Run _main.py in folder pretraining (set EMB_DIM as 128, 64, 42, and 32 in p_params.py).
    We also provide downloading for pretraining:
        https://drive.google.com/file/d/1UV3KO_5wOKkr4v5FePD19cVGhGifz4SR/view?usp=sharing
        https://pan.baidu.com/s/1KuI4gHViONl3tzRT1Prv1w (password: 1234)
    You can choose one of these two URLs for downloading (we recommend the first one). Downloaded and unzip LCFN_dataset.zip, and use it to replace the folder dataset in our project.

2. Run _main.py in our project (datasets, hyperparameters can be set in params.py).
    2.1 Tuning models (if you want to change datasets or hyperparameters): We provide a automatic tool to tune models with respect to learning rate \eta and regularization coefficient \lambda. Set "tuning_method" in line 24 in _main.py as 'tuning' and run _main.py. The best \eta and \lambda and corresponding performance can be returned.
    2.2 Testing models: Set "tuning_method" as 'test' and run _main.py.

3. Check results in folder experiment_result. Collect results by result_collection.

******************************************************************************

* In the result_collection folder, we provide a tool for results collection. Please read the manual for details.

* We also release our tuning results in folder supplementary_material.

* In the dataset folder, we prodive Amazon and Movielens to conduct our experiments. For each dataset, we split it to three subsets: train_data, validation_data, and test_data. You can use our processed datasets, or construct them from the raw data by running amazon.py and movielens.py. For the raw data please find on:

1. Amazon 
http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz

2. Movielens
http://grouplens.org/datasetss/movielens/1m

******************************************************************************

Please cite one of our papers if you use our codes:

@inproceedings{LCFN,
	title={Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters},
	author={Yu, Wenhui and Qin, Zheng},
	booktitle = {ICML},
	year={2020}
}

@article{SGNN,
	title={Self-propagation Graph Neural Network for Recommendation},
	author={Yu, Wenhui and Lin, Xiao and Liu, Jinfei and Ge, Junfeng and Ou, Wenwu},
	journal={TKDE},
	year={2021}
}