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SPEX

This repository holds the code for our TOIS paper: SPEX: A Generic Framework for Enhancing Neural Social Recommendation [Paper]. If you find it is useful for your work, please consider citing our paper:

Hui Li, Lianyun Li, Guipeng Xv, Chen Lin, Ke Li, and Bingchuan Jiang, "SPEX: A Generic Framework for Enhancing Neural Social Recommendation," ACM Transactions on Information Systems (TOIS), vol. 40, no. 2, pp. 37:1-37:33, 2022.

@article{LiLXLL22,
  author    = {Hui Li and
               Lianyun Li and
               Guipeng Xv and
               Chen Lin and
               Ke Li and
               Bingchuan Jiang},
  title     = {SPEX: A Generic Framework for Enhancing Neural Social Recommendation},
  journal   = {ACM Transactions on Information Systems (TOIS)},
  year      = {2022},
  volume    = {40},
  number    = {2},
  pages     = {37:1-37:33}
}

Baseline:

  1. NCF: Neural Collaborative Filtering. WWW'17.

  2. NGCF: Neural Graph Collaborative Filtering. SIGIR'19.

  3. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR'20.

  4. GraphRec: Graph Neural Networks for Social Recommendation. WWW'19.

  5. SAMN: Social Attentional Memory Network: Modeling Aspect- and Friend-level Differences in Recommendation. WSDM'19.

  6. DiffNet++: DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. TKDE'20.

  7. DANSER: Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW'19.

  8. FuseRec: FuseRec: Fusing User and Item Homophily Modeling with Temporal Recommender Systems. DMKD'21.

Environment:

  • Python 3.7
  • Pytorch 1.5.1
  • Tensorflow 2.0.0.

Project structure:

│  README.md
│  
├─XXX_SPEX
│  └─code
│  │   │  main_rec.py
│  │   │  main_11.py
│  │   │  main_auto.py
│  │   │  main_auto_expert_s.py
│  │   │  main_cross.py
│  │   │  main_oy.py
│  │   │  
│  │   ├─utility       
│  │   └─utility2
│  │  
│  └─Data
│      ├─epinion2
│      │  ├─rec     
│      │  └─trust       
│      ├─twitter
│      │  ├─rec   
│      │  └─trust      
│      └─weibo
│          ├─rec     
│          └─trust
│              
└─Trust_SPEX

Datasets:

The method of processing the dataset is in the folder Data_process.

You can get the dataset by processing the raw data or downloading from google driver

Train:

1. Run both recommendation task and path prediction task.

Enter the directory (for example, you can do the following to run LightGCN).

cd SPEX/LightGCN_SPEX/code

Train the model (you can use different datasets and gpu id, and choose the number of heads for SPEX).

User Embedding sharing mode: Direct-Sharing

python main_11.py  # 1:1 for multi task loss weight

python main_auto.py / main_auto_expert_s.py   # automatically set task weights

User Embedding sharing mode: Cross-Stitch

python main_cross.py

User Embedding sharing mode: Shared-Private

python main_oy.py

2. Run the recommendation task only.

python main_rec.py

3. Run the path prediction task only.

cd Trust_SPEX/code
python trust.py

Notes:

To achieve good results, pay attention to the selection of parameters. For instance, LightGCN needs:

--dropout=1 --keepprob=0.3

and you will get similar results after about 50 epochs.

For more details, you can refer to our paper.