- Lei Li, Yongfeng Zhang, Li Chen. On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance. ACM Transactions on Intelligent Systems and Technology (TIST), 2022.
- Lei Li, Yongfeng Zhang, Li Chen. EXTRA: Explanation Ranking Datasets for Explainable Recommendation. SIGIR'21 Resource.
Datasets to download
- Amazon Movies & TV
- TripAdvisor Hong Kong
- Yelp 2019
If you are interested in how to create the datasets, please refer to EXTRA.
Below is an example of how to run BPER+.
python -u run_bperp.py \
--cuda \
--data_dir ../Amazon/ \
--index_dir ../Amazon/2/ \
--lr 0.0001 >> bperp.log
- Download the three files and put them in a folder, e.g., ./bert-base-uncased/
- Run the program
python -u run_bperp.py \
--cuda \
--data_dir ../Amazon/ \
--index_dir ../Amazon/2/ \
--model_name ./bert-base-uncased/ \
--lr 0.0001 >> bperp.log
- If you want to do follow-up works on our BPER/BPER-J, please modify the code of BPER+, as it is more efficient.
- If you do so, please set the maximum iteration number to a relatively large value, e.g.,
--epochs 50
.
- Python 3.6
- PyTorch 1.6
@article{TIST22-BPER,
title={On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance},
author={Li, Lei and Zhang, Yongfeng and Chen, Li},
journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
year={2022}
}
@inproceedings{SIGIR21-EXTRA,
title={EXTRA: Explanation Ranking Datasets for Explainable Recommendation},
author={Li, Lei and Zhang, Yongfeng and Chen, Li},
booktitle={SIGIR},
year={2021}
}