• OAAG
• • Dataset
  • Citation

OAAG

The code and data preparation is based on this repo.

Dataset

This is an benchmark dataset for opinion-aware answer generation in E-Commerce. You can downloaded the processed dataset via the following url:

https://drive.google.com/file/d/19012ClEam378QzPWL8ww8HMpBcjye_Rf/view?usp=sharing

Please cite the following paper if you use it in any way：

• Yang Deng, Wenxuan Zhang, Wai Lam. Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce. In The 29th ACM International Conference on Information and Knowledge Management (CIKM 2020).

Also, because this benchmark is built from the Amazon QA datasets and Amazon review datasets provided by McAuley et al. (https://cseweb.ucsd.edu/~jmcauley/datasets.html), please also cite their papers following if you use the data:

• Julian McAuley, Alex Yang. Addressing complex and subjective product-related queries with customer reviews. In World Wide Web (WWW), 2016.
• R. He, J. McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In World Wide Web (WWW), 2016.

Citation

If the data or code is used in your research, please star this repo and cite our paper as follows:

@inproceedings{DBLP:conf/cikm/DengZL20,
  author    = {Yang Deng and
               Wenxuan Zhang and
               Wai Lam},
  title     = {Opinion-aware Answer Generation for Review-driven Question Answering
               in E-Commerce},
  booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
               and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
  pages     = {255--264},
  year      = {2020},
  url       = {https://doi.org/10.1145/3340531.3411904},
}