This code is released for the paper:
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Diffusion Model for Social Recommendation. Accepted by SIGIR2019.
You can also preview our paper from the arXiv.
- Environment: I have tested this code with python2.7, tensorflow-gpu-1.12.0
- Download the yelp data from this link, and unzip the directories in yelp data to the root directory of your local clone repository.
- cd the diffnet directory and execute the command
python entry.py --data_name=<data_name> --model_name=<model_name> --gpu=<gpu id>then you can see it works, if you have any available gpu device, you can specify the gpu id, or you can just ignore the gpu id.
Following is an example: python entry.py --data_name=yelp --model_name=diffnet
The dataset flickr we use from this paper: @article{HASC2019, title={A Hierarchical Attention Model for Social Contextual Image Recommendation}, author={Le, Wu and Lei, Chen and Richang, Hong and Yanjie, Fu and Xing, Xie and Meng, Wang}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2019} } The algorithm is from tis paper: @inproceedings{DiffNet2019. title={A Neural Influence Diffusion Model for Social Recommendation}, author={Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang}, conference={42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, year={2019} } If you want to know more details about how to tackle the graph oversmoothing issue, please refer to: @inproceedings{ title={Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach}, author={Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang}, conference={The 34th AAAI Conference on Artificial Intelligence (AAAI 2020)}, year={2020} } Email: sun.hfut@gmail.com
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