The source code and dataset for the RecGURU paper (WSDM 2022)

Related tags

Deep Learning adversarial-learning sequential-recommendation cross-domain-recommendation dual-target
Overview

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/1NbP48emGPr80nL49oeDtPDR3R8YEfn4J/view
  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"
Comments
  • 请问collected_data如何处理？

    请问collected_data如何处理？

    Hi, Li:

    我在下载完collected_data.gz之后，解压得到collected_data，但是这似乎无法打开得到相应的train/valid/test。

    另外business_process.py似乎也不能直接处理collected_data.gz这个文件。

    请问应该如何得到处理后的数据呢？

    opened by caojiangxia 8
  • Fused with which Amazon view dataset should be download

    Fused with which Amazon view dataset should be download

    Hello, I,m not clear which Amazon view dataset should be download from :https://nijianmo.github.io/amazon/index.html ,there are any clues? Also, is it convenient to provide the environmental configuration of the project?

    opened by CreaterLL 4
  • business_process.py 文件没有生成kdd_10_f 只有kdd_10

    business_process.py 文件没有生成kdd_10_f 只有kdd_10

    image business_process.py文件只生成了kdd_10文件 ，为什么会显示kdd_10_f文件找不到呢 我需要将kdd_10_f直接改成kdd_10吗

    另外 代码中需要freq的文件 但是business_process.py并没有在kdd_10中文件夹中生成freq文件，请问这里的freq文件是需要自己生成吗？

    opened by CreaterLL 2
  • Confused with the result_a.* file

    Confused with the result_a.* file

    hi，after running ‘python train_gan.py --cross "True"’，i got only result_a.* files and the content is like as image 、 image ,i'm so confused with them. and i haven't found the HR@metrics. could you please give me some instructions? besides,i 'm also confused with if there are both results of two domains in the result_a. files？ Thank you!

    opened by CreaterLL 1
  • Exception on processing the collected data

    Exception on processing the collected data

    I follow the guideline to download the kdd.tar.gz from provided URL and run the business_process py file.

    But some exceptions happen in the generate_data function. It happens in lines 209-249. except Exception as e: print(data_t["uid"][u_id]) print(data_t["a_item"][u_id]) print(wesee_items) print(data_t["b_item"][u_id]) print(video_items) sys.exit()

    Is my download file is broken? The MD5 value of my download kdd.tar.gz is "a7e572a892b602552eaaa4203a8d7f14".

    opened by WujiangXu 4
Owner
Chenglin Li
Chenglin Li
GitHub
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