RecGURU

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RecGURU

About The Project

Source code and baselines for the RecGURU paper "RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation (WSDM 2022)"

Code Structure

RecGURU  
├── README.md                                 Read me file 
├── data_process                              Data processing methods
│   ├── __init__.py                           Package initialization file     
│   └── amazon_csv.py                         Code for processing the amazon data (in .csv format)
│   └── business_process.py                   Code for processing the collected data
│   └── item_frequency.py                     Calculate item frequency in each domain
│   └── run.sh                                Shell script to perform data processing  
├── GURU                                      Scripts for modeling, training, and testing 
│   ├── data                                  Dataloader package      
│     ├── __init__.py                         Package initialization file 
│     ├── data_loader.py                      Customized dataloaders 
│   └── tools                                 Tools such as loss function, evaluation metrics, etc.
│     ├── __init__.py                         Package initialization file
│     ├── lossfunction.py                     Customized loss functions
│     ├── metrics.py                          Evaluation metrics
│     ├── plot.py                             Plot function
│     ├── utils.py                            Other tools
│  ├── Transformer                            Transformer package
│     ├── __init__.py                         Package initialization 
│     ├── transformer.py                      transformer module
│  ├── AutoEnc4Rec.py                         Autoencoder based sequential recommender
│  ├── AutoEnc4Rec_cross.py                   Cross-domain recommender modules
│  ├── config_auto4rec.py                     Model configuration file
│  ├── gan_training.py                        Training methods of the GAN framework
│  ├── train_auto.py                          Main function for training and testing single-domain sequential recommender
│  ├── train_gan.py                           Main function for training and testing cross-domain sequential recommender
└── .gitignore                                gitignore file

Dataset

1. The public datasets: Amazon view dataset at: https://nijianmo.github.io/amazon/index.html
2. Collected datasets: https://drive.google.com/file/d/1Eszu-mApyzvVj6tAunPuYql6aIJlQmhg/view?usp=sharing (tar.gz) or https://drive.google.com/file/d/1NbP48emGPr80nL49oeDtPDR3R8YEfn4J/view (.gz file use 7z to unzip)
3. Data processing:

Amazon dataset:

```shell
cd ../data_process
python amazon_csv.py   
```

Collected dataset

```shell
cd ../data_process
python business_process.py --rate 0.1  # portion of overlapping user = 0.1   
```

After data process, for each cross-domain scenario we have a dataset folder:

."a_domain"-"b_domain"
├── a_only.pickle         # users in domain a only
├── b_only.pickle         # users in domain b only
├── a.pickle              # all users in domain a
├── b.pickle              # all users in domain b
├── a_b.pickle            # overlapped users of domain a and b   

Note: see the code for processing details and make modifications accordingly.

Run

1. Single-domain Methods:

   # SAS
   python train_auto.py --sas "True"
   # AutoRec (ours)
   python train_auto.py 
   

2. Cross-Domain Methods:

   # RecGURU
   python train_gan.py --cross "True"