This is our Tensorflow implementation for our GCARec 2022 paper and a part of baselines:
Bin Wu, Xiangnan He, Le Wu, Xue Zhang, Yangdong Ye. Graph Augmented Co-Attention Model for Socio-Sequential Recommendation, IEEE Transactions on Systems, Man and Cybernetics: Systems, 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\GCARec.properties
(3) set NeuRec parameters on .\NeuRec.properties
(4) the log file save at .\log\Ciao_u5_s2\
We provide Ciao_u5_s2(Ciao) dataset.
- .\dataset\Ciao_u5_s2.rating and Ciao_u5_s2.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 GCARec, 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. |
Sequential Recommender | Paper |
---|---|
FPMC | S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, Factorizing personalized markov chains for next-basket recommendation, WWW, 2010. |
HGN | C. Ma, P. Kang, and X. Liu, Hierarchical gating networks for sequential recommendation, KDD, 2019. |
Social Recommender | Paper |
---|---|
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. |