Basic Information:
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
Usage:
- Environment: I have tested this code with python2.7, tensorflow-gpu-1.12.0
- 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
Citation:
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}
}
Author contact:
Email: [email protected], [email protected]
从以上代码可以看出 gama^(k+1)(a1) =1/2* self.consumed_items_attention,gama^(k+1)(a2) =1/2* self.social_neighbors_attention。gama^(k+1)(a1)和gama^(k+1)(a2)也确实是利用了使用到MLP的GAT实现的。
说alpha^(k+1)(ab)和beta^(k+1)(ai)的计算过程也是和gama的计算过程类似，使用MLP借助两个embedding得到。但是，我去看了源码中beta^(k+1)_(ai)的计算过程，我发现，beta与gama的计算过程存在差别，感觉并没有借助两个embedding，倒像是随机产生的。
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