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Official code for "Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation"

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336ee1b · Nov 21, 2024

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HGCDR

1. Background

This project is the implementation for 'Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation'

Link to our paper: https://arxiv.org/abs/2407.00909

2. Install & Run

  1. Make sure that your virtual env has the following packages:

    1. PyTorch (GPU Ver recommended)
      pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
    2. Numpy
    3. Pandas
    4. DGL (0.7.2 ver)
      pip install dgl-cu113==0.7.2 -f https://data.dgl.ai/wheels/repo.html
    5. Tensorboard
    6. Scikit-Learn
  2. download the project from the repository

  3. download the Douban data from the url: https://github.com/fengzhu1/GA-DTCDR/tree/main/Data, and move the dataset to the dir: ./data/doudan

  4. run the command in the virtual env: if you want to run the Douban data

    python src/model_douban.py
  5. the whole project support NVIDIA GPU ACCELERATION.

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Official code for "Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation"

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