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This is our implementation for the paper: Pan Li and Alexander Tuzhilin. "DDTCDR: Deep Dual Transfer Cross Domain Recommendation." Proceedings of the 13th International Conference on Web Search and Data Mining. 2020

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DDTCDR

This is our implementation for the paper:

Pan Li and Alexander Tuzhilin. "DDTCDR: Deep Dual Transfer Cross Domain Recommendation." Proceedings of the 13th International Conference on Web Search and Data Mining. 2020. [Paper]

Important: Due to the confidential agreement with the company, we are not allowed to make the dataset publicly available. Nevertheless, we provide a sample of the dataset for you to get an understanding of the input strcuture. You are always welcome to use our codes for your own dataset.

Please cite our WSDM'20 paper if you use our codes. Thanks!

Author: Pan Li (https://lpworld.github.io/)

Environment Settings

We use PyTorch as the backend.

  • PyTorch version: '1.2.0'

Example to run the codes.

The instruction of commands has been clearly stated in the codes (see the parse_args function).

Run DDTCDR:

python train.py

Acknowledgement

This implementation is inspired from Neural Collaborative Filtering. The authors would also like to thank Vladimir Bobrikov for providing the dataset for evaluation purposes.

Last Update: 2020/02/16

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This is our implementation for the paper: Pan Li and Alexander Tuzhilin. "DDTCDR: Deep Dual Transfer Cross Domain Recommendation." Proceedings of the 13th International Conference on Web Search and Data Mining. 2020

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