GCARec

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

Environment Requirement

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

C++ evaluator

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.**

Examples to run GCARec:

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\

Dataset

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.

Baselines

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.

wubinzzu / GCARec