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Explainable Recommendation with Comparative Constraints on Product Aspects

This is the code for the paper:

Explainable Recommendation with Comparative Constraints on Product Aspects <br> Trung-Hoang Le and Hady W. Lauw <br> Presented at WSDM 2021

If you find the code and data useful in your research, please cite:

@inproceedings{10.1145/3437963.3441754,
  title     = {Explainable Recommendation with Comparative Constraints on Product Aspects},
  author    = {Le, Trung-Hoang and Lauw, Hady W.},
  year      = {2021},
  isbn      = {9781450382977},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3437963.3441754},
  doi       = {10.1145/3437963.3441754},
  booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages     = {967–975},
  numpages  = {9},
  keywords  = {explainable recommendation, comparative constraints},
  location  = {Virtual Event, Israel},
  series    = {WSDM '21}
}

How to run

pip install -r requirements.txt

There are two variants of ComparER model: subjective and objective.

Run ComparER on Subjective Aspect-Level Quality

Run MTER model:

python mter.py

MTER is the base model of ComparER with subjective aspect-level quality. After finish training MTER, we can continue train ComparERSub by the command:

python comparer_sub.py

Run ComparER on Objective Aspect-Level Quality

Run EFM model:

python efm.py

EFM is the base model of ComparER with objective aspect-level quality. After finish training EFM, we can continue train ComparERObj by the command:

python comparer_obj.py

Contact

Questions and discussion are welcome: lthoang.com