This is a pytorch implementation of our ACM Transactions on Information Systems (ACM TOIS) paper: "Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation"
Running Tips
This folder contains the thhree datasets used in our paper and the codes of our ETL model. Specifically, main_my.py is our model and the other four main_xxx.py files are four variants of ETL in the abalation study of our paper.
The directory originization
ETL-master/
----processed_data/
----saved_embeddings/my/none.txt
----code_name1.py
----code_name2.py
0. Data preparation:
Download the processed data files from Google drive: https://drive.google.com/drive/folders/1oIABLZE0UcEylLwiJrWY5R3rw7t739Zz?usp=sharing
and Baidu disk: https://pan.baidu.com/s/1UD5ezVqjsMBmIgQXl1CA-g password:yk2i.
When you download the processed_data, you can replace the dir here and directly run our codes! Note that the data_filter_amazon.py under processed_data dir is the file that pre-processes the original data. You can skip the pre-processing step if you download the processed data files.
1. Requirements:
python3.5; pytorch=1.4.0; tqdm=4.27.0; tensorboardX=1.8; pandas=0.25; numpy=1.15; networkx=2.2; logger=1.4; scipy=1.1; scikit-learn=0.20
2.Runing commands
Run the codes with the following commands on different datasets (amazon means "Movie & Book", amazon2 means "Movie & Music" and amazon3 means "Music & Book").
-->on Movie & Book dataset: CUDA_VISIBLE_DEVICES=gpu_num python main_my.py --dataset amazon --reg 5.0
-->on Movie & Music dataset:
CUDA_VISIBLE_DEVICES=gpu_num python main_my.py --dataset amazon2 --reg 0.5
-->on Music & Book dataset:
CUDA_VISIBLE_DEVICES=gpu_num python main_my.py --dataset amazon3 --reg 1.0
In this way, you can get he results. Besides, if you want to run the variants of ETL, just follow the same way while with different main files.
Moreover, if you want to do experments with different data sparsity, you can add "--s_percent ${ratio} --t_percent ${ratio}" to the above running commands.
If you find this paper or codes useful, please cite our paper. Thank you!
@article{chen2020equivalent,
title={Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation},
author={Xu Chen and Ya Zhang and Ivor Tsang and Yuangang Pan and Jingchao Su},
year={2020},
eprint={2009.06884},
archivePrefix={ACM Transactions on Information Systems},
}