GDCCDR

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The code for AAAI2024 paper "Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation"

GDCCDR 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# GDCCDR
The source code is official pytorch implementation of GDCCDR (Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation) by Jing Liu, Lele Sun, Weizhi Nie, PeiGuang Jing and Yuting Su.
```@inproceedings{GDCCDR,
  title={Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation},
  author={Liu, Jing and Sun, Lele and Nie, Weizhi and Jing, Peiguang and Su, Yuting},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={8},
  pages={8769--8777},
  year={2024}
}
```
# Requirements:
* Python == 3.8.13
* PyTorch == 1.11.0
* torch-sparse == 0.6.13
* numpy == 1.22.3
# Datasets
We use four [Amazon](http://jmcauley.ucsd.edu/data/amazon/) datasets (Sport&Phone, Sport&Cloth, Elec&Phone, Elec&Cloth) to evaluate our GDCCDR. We preprocess these datasets following [BI-TGCF](https://github.com/sunshinelium/Bi-TGCF) and filter out the cold-start items.
# Training
You can use these commands to train the model:
* python main.py --dataset sport_phone --ecl_reg 0.2 --pcl_reg 0.001 --alpha 0.25 --beta 0.03 --layer 6
* python main.py --dataset sport_cloth --ecl_reg 0.05 --pcl_reg 0.05 --alpha 0.1 --beta 0.3 --layer 5

GDCCDR

GDCCDR 최신버젼 다운로드

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