This is our Tensorflow implementation for our GAST 2023 paper and a part of baselines:
Bin Wu, Lihong Zhong & Yangdong Ye. Graph-augmented social translation model for next-item recommendation, IEEE Transactions on Industrial Informatics (TII), Accept
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\GAST.properties
(3) set NeuRec parameters on .\NeuRec.properties
(4) the log file save at .\log\Gowalla_yiding_u5_s3\
We provide Gowalla_yiding_u5_s3(Gowalla) dataset.
- .\dataset\Gowalla_yiding_u5_s3.rating and Gowalla_yiding_u5_s3.uu
- Each line is a user with her/his positive interactions with items: userID \ itemID \ ratings \time.
- Each user has more than 10 associated actions.
The list of available models in GAST, 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. |
| LightGCN | Xiangnan He, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020. |
| SGL | J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie. Self supervised graph learning for recommendation. SIGIR, 2021. |
| Sequential Recommender | Paper |
|---|---|
| SASRec | W. Kang and J. J. McAuley. Self-attentive sequential recommendation. ICDM, 2018. |
| TransRec | R. He, W. Kang, and J. McAuley. Translation-based recommendation. RecSys, 2017. |
| Social Recommender | Paper |
|---|---|
| EATNN | C. Chen, M. Zhang, C. Wang, W. Ma, M. Li, Y. Liu, and S. Ma. An efficient adaptive transfer neural network for social-aware recommendation. SIGIR, 2019. |
| EAGCN | B. Wu, L. Zhong, L. Yao, and Y. Ye. EAGCN: An efficient adaptive graph convolutional network for item recommendation in social internet of things, IOT, 2022. |