UNBERT is a BERT-based user-news matching model that leverages the use of the successful BERT pre-training technique for news recommendation. In contrast to existing research, the UNBERT model not only leverages the pre-trained model with rich language knowledge to enhance textual representation, but also captures multi-grained user-news matching signals at both word-level and newslevel.
Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, Xiuqiang He. UNBERT: User-News Matching BERT for News Recommendation, in IJCAI 2021.
pip install -r requirements.txtFor the MIND dataset, please download at https://msnews.github.io
| File Name | Description |
|---|---|
| data/bert-base-uncased | pretrained model from huggingface |
| data/small/train | MIND-small train dataset |
| data/small/dev | MIND-small dev dataset |
| data/large/train | MIND-large train dataset |
| data/large/dev | MIND-large dev dataset |
| data/large/test | MIND-large test dataset |
python run.py --mode train --split small --root ./data/ --pretrain data/bert-base-uncased/For more hyper-parameter settings, please refer to run.py.