chain123 / cat-art

One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation (WSDM-2023)

One for All, All for One

About The Project

Multi-target Cross-domain recommendation with implicit feedback data in each domain. Paper under submission to CIKM-22, "One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation".

Code Structure

One-For-All   
├── README.md                                 Read me file
├── bert                                      Bert modules and Transformer layers
├── data                                      Demo data  
├── dataset                                   Data set and processing methods
│  ├── check_sparsity.ipynb                   Calculate data set distribution for all domains.   
│  ├── multi-domain_amazon.ipynb              Demo on how to process the Amazon multiple data sets for MTCDR   
├── utils                                     Utilities, e.g. early stop.
│  ├── __init__.py                            Module init 
│  ├── data.py                                All data loaders 
│  ├── eval_metrics.py                        Evaluation metrics 
│  ├── loss_func.py                           Loss functions 
│  ├── pytorchtools.py                        Early stop implementation
│  ├── result_plot.py                         Line plot function 
│  ├── Save_embedding.py                      Functions for saving item/user embedding, preference scores, etc. 
│  ├── scheduler.py                           Learning rate scheduler
│  ├── tools.py                               Others (not in use)
│── Data_loader.py                            Entry for data split and data loader creators      
├── loss_functions.py                         Loss functions
├── main_cross.py                             CAT-ART model, without pre-training of the CAT module 
├── main_cross_pre.py                         CAT-ART model, with pre-training of the CAT module
├── main_cross_scores.py                      Get the averaged attention scores of the ART module on the test set.
├── main_single_mf.py                         Single domain MF model for recommendation
├── models.py                                 Models 
├── run_functions.py                          Run functions, e.g. train step, test step
├── show_result.py                            Printout result from the pickle file
├── run.sh                                    General run entry on Venus 
├── run_cross.sh                              My run demos with parameters 
├── run_cross_auto.sh                         My run demos with parameters
├── run_cross_pre.sh
└── .gitignore                                gitignore file

Packages

pip install torch
pip install sklearn

Usage

1. Dataset and process

   1.1 Original Full Data Processing Stream

    # Train data at '/data/ceph/seqrec/UMMD/data/hdfs/q36_age_train_08' (17G);
    cd bert/dataset
    # for each file in the train data folder run the following:
    python data_txt2pickle.py --filename "demo_data.gz" --domain 0
    # Results were saved at: /data/ceph/seqrec/UMMD/data/pickle/q36_age_train_org
    
    cd ../../   # Return to the main folder 
    ipython notebook
    run the train_test_split.ipynb  # for train and test splitting (only for missing 0 data samples)
    # Results were saved to:/data/ceph/seqrec/UMMD/data/pickle/q36_age_train_rec2  

   Here we provide a single processed file for both train and test as demo data at ./data folder.

   We are still discussing whether and how to open-source the original dataset.

   1.2 Data Structure of used pickle file

   Each pickle file stores a dict data with keys: 'uid', 'age', 'gender', and 'feature', in which the values of keys 'uid', 'age', 'gender' are list data. The value for the 'feature' key is a list of length 5, where each element represent the 'features' (interacted items) in each of the 5 domains. For example:

   'uid': [1, 2, 3],
   'age': [11, 21, 31],
   'gender': [1, 2, 1], # 1-male, 2-female
   'feature': [
               [[a, b], [1, 2, 1], [i,j, k,l]],  # user 'feature' in domain 0 (for the three users)
               [[b, c], [1, 2, 4], [i,k,l]],  # user 'feature' in domain 1 (for the three users)
               [[a, d], [1, 2, 10], [k,l]],  # user 'feature' in domain 2 (for the three users)
               [[d, b], [1,2, 6], [1, l]],  # user 'feature' in domain 3 (for the three users)
               [[g, b], [1, 2, 5, 6], [i,h, p]],  # user 'feature' in domain 4 (for the three users)
               ]           
   

2. Single-domain recommendation

   configure your dataset path in function train_test_split() at Data_loader.py

   # Single domain recommendation with BPR based MF
   python main_single_mf.py --batch_size 2048 --num_run 0 --domain 0 --epoch 20 --bar_dis True --result_dir 'your path' --train True

3. Multi-target cross-domain recommendation

   # CAT-ART
   python main_cross.py --result_dir 'your path'
   python main_cross_pre.py --result_dir 'your path'

   For baseline HeroGRAPH, please refer to their open source at: https://github.com/cuiqiang1990/HeroGRAPH

Contact

Chenglin Li: ch11 AT ualberta dot ca

cat-art's Issues

About the class Quantize in .vq

Hello Li, this paper is an excellent job.

I found a problem when trying to reproduce it, the implementation of class Quantize in .vq is not included in the open source code, could you add the implementation of that class? Thank you to a great deal!

Hello Li, about the amazaon dataset

Hello Li, I read your article recently, and I want to run the code, but failed. If the dataset in your paper can't be released, could you please give me the amazon dataset that has been processed?

And the implementation of class Quantize in .vq is not included in the open source code, is it unnecessary?

Best wishes!

Hello Li , about the code release and dataset

Hi I read your article on arxiv, your work is very advanced work in CDR, I would like to know more about this work, when can I see the code? Is it possible to provide the dataset😄 ?

Thanks !