AkiraZC / CATN

The implementation of SIGIR 2020 paper "CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network“, Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng and Aixin Sun

CATN

Codes for SIGIR 2020 paper CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network.

Citation

Please cite our paper if you find this code useful for your research:

@inproceedings{sigir20:catn,
  author    = {Cheng Zhao and
               Chenliang Li and
               Rong Xiao and
               Hongbo Deng and
               Aixin Sun},
  title     = {{CATN:} Cross-Domain Recommendation for Cold-Start Users via Aspect
               Transfer Network},
  booktitle = {{SIGIR}},
  year      = {2020},
}

Requirement

• python 3.6
• tensorflow 1.10.0
• numpy
• pandas
• scipy
• gensim
• sklearn
• tqdm

Files in the folder

• dataset/
  • preprocessing.py: constructing cross-domain datasets;
• runner/
  • CATN_runner.py: the main runner (including the configurations);
• utils
  • CATN.py: CATN implementation.

Running the code

1. Download the original data from Amazon-5core, choose two relevant categories (e.g., Books, Movies and TV) and put them under the same directory in dataset/.

2. run python preprocessing.py.

3. run python CATN_runner.py.