This is our Tensorflow implementation for our EAGCN 2022 paper and a part of baselines:
Bin Wu, Lihong Zhong, Lina Yao, Yangdong Ye. EAGCN: An Efficient Adaptive Graph Convolutional Network for Item Recommendation in Social Internet of Things, IEEE Internet of Things Journal, Accept, 2022, PDF.
The code has been tested running under Python 3.6.5. The required packages are as follows:
- tensorflow == 1.14.0
- numpy == 1.16.4
- scipy == 1.3.1
- pandas == 0.17
We have implemented C++ code to output metrics during and after training, which is much more efficient than python evaluator. It needs to be compiled first using the following command.
python setup.py build_ext --inplace
If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called. NOTE: The cpp implementation is much faster than python.**
run main.py in IDE or with command line:
python main.py
NOTE :
(1) the duration of training and testing depends on the running environment.
(2) set model hyperparameters on .\conf\EAGCN.properties
(3) set NeuRec parameters on .\NeuRec.properties
(4) the log file save at .\log\yelp_gat\
We provide yelp_gat(yelp) dataset.
- .\dataset\yelp_gat.rating and yelp_gat.uu
- Each line is a user with her/his positive interactions with items: userID \ itemID \ ratings .
- Each user has more than 10 associated actions.
The list of available models in EAGCN, along with their paper citations, are shown below:
| General Recommender | Paper |
|---|---|
| BPRMF | Steffen Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. |
| NGCF | Xiang Wang, et al., Neural Graph Collaborative Filtering. SIGIR 2019. |
| LightGCN | Xiangnan He, et al., LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020. |
| Social Recommender | Paper |
|---|---|
| EATNN | C. Chen et al., An efficient adaptive transfer neural network for social-aware recommendation SIGIR 2019. |