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.gitignore

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README.md

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+# Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding
+
+*Xinzhou Jin, Jintang Li, Yuanzhen Xie, Liang Chen, Beibei Kong, Lei Cheng, Bo Hu, Zang Li, Zibin Zheng*
+
+This is the official PyTorch implementation of our paper that has been accepted to 2023 IEEE International Conference on Data Mining (ICDM 2023). [[Paper]]()
+
+<img src="./figure/fig.png" alt="fig" style="zoom:100%;" />
+
+
+
+## Requirements:
+
+```
+python>=3.9.13
+pytorch>=1.12.1
+torch-geometric>=2.2.0
+torch-sparse>=0.6.15+pt112cu116
+numpy>=1.24.3
+pandas>=1.5.0
+CUDA 11.6
+```
+
+
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+# PyTorch and PyG depend on your own cuda environment. The following instructions are for CUDA11.6.
+pip install torch==1.12.1+cu116 -f https://download.pytorch.org/whl/torch/
+pip install torch-sparse==0.6.16 -f https://pytorch-geometric.com/whl/torch-1.12.1+cu116.html
+pip install torch-scatter==2.1.0 -f https://pytorch-geometric.com/whl/torch-1.12.1+cu116.html
+pip install torch-geometric
+```
+
+
+
+## Datasets
+
+| Datasets | #Users  | #Items | #Interactions | Density |
+| -------- | ------- | ------ | ------------- | ------- |
+| ML-1M    | 6,039   | 3,628  | 836,478       | 0.03818 |
+| Yelp     | 45,477  | 30,708 | 1,777,765     | 0.00127 |
+| Books    | 58,144  | 58,051 | 2,517,437     | 0.00075 |
+| Gowalla  | 29,858  | 40,988 | 1,027,464     | 0.00084 |
+| Alibaba  | 300,000 | 81,614 | 1,607,813     | 0.00007 |
+
+For `ml-1m` , `yelp`, `amazon-books`, `gowalla-merged`, they will be automatically downloaded via RecBole once you run the main program.
+
+For `alibaba`, we provide it under `dataset/`
+
+```
+cd dataset
+unzip alibaba.zip
+```
+
+
+
+## Implementation of NSE:
+
+NSE is easy to implement as follows (PyTorch-style):
+
+```python
+def get_neighbor_adj(self):
+    from torch_sparse import SparseTensor
+    sp_adj = SparseTensor(row=self._user, col=self._item, value=torch.ones(
+        len(self._user)), sparse_sizes=(self.n_users, self.n_items))
+    return sp_adj
+
+
+def get_ego_embeddings(self):
+        r""" Get the embedding of users and items and combine to an embedding matrix.
+        Returns:
+            Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim]
+        """
+        from torch_geometric.utils import spmm
+        user_embeddings = spmm(self.sp_adj, self.item_embedding.weight).to_dense()
+        item_embeddings = spmm(self.sp_adj.t(), self.user_embedding.weight).to_dense()
+        ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0)
+
+        return ego_embeddings
+```
+
+
+
+## Reproduction
+
+We integrate our NSE-LGCN method into the [RecBole](https://recbole.io/) and [RecoBole-GNN](https://github.com/RUCAIBox/RecBole-GNN) framework.
+
+#### ML-1M
+
+
+```
+python run_recbole_gnn.py --dataset "ml-1m" --model "NSELightGCN" --n_layers 3 --reg_weight 0.0001 --learning_rate 0.0002
+```
+
+#### Yelp
+
+
+```
+python run_recbole_gnn.py --dataset "yelp" --model "NSELightGCN" --n_layers 3 --reg_weight 0.01 --learning_rate 0.0001
+```
+
+#### Amazon-books
+
+
+```
+python run_recbole_gnn.py --dataset "amazon-books" --model "NSELightGCN" --n_layers 3 --reg_weight 0.001 --learning_rate 0.0001
+```
+
+#### Gowalla
+
+
+```
+python run_recbole_gnn.py --dataset "gowalla-merged" --model "NSELightGCN" --n_layers 3 --reg_weight 0.0001 --learning_rate 0.0001
+```
+
+#### Alibaba
+
+
+```
+python run_recbole_gnn.py --dataset "alibaba" --model "NSELightGCN" --n_layers 3 --reg_weight 1e-06 --learning_rate 0.0001
+```
+
+
+
+
+
+If you want to run on the synthetic datasets, add `--ptb_strategy=replace` to the above commands. For example:
+
+```
+python run_recbole_gnn.py --dataset "ml-1m" --model "NSELightGCN" --n_layers 3 --reg_weight 0.01 --learning_rate 0.0001 --ptb_strategy=replace
+```
+
+
+
+## Acknowledgement
+
+This repo is mainly based on [RecBole](https://recbole.io/) and [RecoBole-GNN](https://github.com/RUCAIBox/RecBole-GNN). Many thanks to their wonderful work!
+
+
+
+## Citation
+
+If you find this work is helpful to your research, please consider citing our paper:
+
+```
+@inproceedings{jin2023enhancing,
+  title={Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding},
+  author={Xinzhou Jin and Jintang Li and Yuanzhen Xie and Liang Chen and Beibei Kong and Lei Cheng and Bo Hu and Zang Li and Zibin Zheng},
+  booktitle={ICDM},
+  year={2023}
+}
+```
+
+
+
+## Contact
+
+If you have any questions about this work, please feel free to contact me via [email protected]

recbole/__init__.py

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+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+__version__ = "1.1.1"

recbole/config/__init__.py

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+from recbole.config.configurator import Config