We have further utilized the nearest-neighbors collaborative signal to enhance the performance of LightGCN, SGL. The new work can refer https://github.com/PeiJieSun/NESCL.
As the python 2.7 is deprecated, I have convert the diffnet code into a new one to make it can be used under python 3.x. If you use python 3.x, tensorflow-gpu-1.x, you can run the code in directory diffnet-tensorflow-v1-python3. I have tested the development environment python 3.7, and tensorflow-1.15.
This code is released for the papers:
Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Diffusion Model for Social Recommendation. Accepted by SIGIR2019. pdf.
Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. Accepted by IEEE Transactions on Knowledge and Data Engineering in Dec 2020. pdf
- Environment: If you use python2.7, tensorflow-gpu-1.12.0, you can run the code in directory diffnet-tensorflow-v1; if you use python 3.7, tensorflow-gpu-1.15, you can run the code in directory diffnet-tensorflow-v1-python3.
- Run DiffNet:
- Download the yelp data from this link, and unzip the directories in yelp data to the sub-directory named diffnet of your local clone repository.
- cd the sub-directory diffnet and execute the command
python entry.py --data_name=<data_name> --model_name=diffnet --gpu=<gpu id>
- Run DiffNet++:
- Download datasets from this link, and just put the downloaded folder 'data' in the sub-directory named diffnet++ of your local clone repository.
- cd the sub-directory diffnet++ and execute the command
python entry.py --data_name=<data_name> --model_name=diffnetplus --gpu=<gpu id>
- If you have any available gpu device, you can specify the gpu id, or you can just ignore the gpu id.
Following are the command examples:
python entry.py --data_name=yelp --model_name=diffnet
python entry.py --data_name=yelp --model_name=diffnetplus
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 DiffNet and DiffNet++: @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} } @article{wu2020diffnet++, title={DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation}, author={Wu, Le and Li, Junwei and Sun, Peijie and Ge, Yong and Wang, Meng}, journal={arXiv preprint arXiv:2002.00844}, year={2020} } We utilized the key technique in following paper to tackle the graph oversmoothing issue, and we have annotated the change in line 114 in diffnet/diffnet.py, if you want to konw more details, 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} }