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https://github.com/yusanshi/news-recommendation
Implementations of some methods in news recommendation.
https://github.com/yusanshi/news-recommendation
Last synced: 3 months ago
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Implementations of some methods in news recommendation.
- Host: GitHub
- URL: https://github.com/yusanshi/news-recommendation
- Owner: yusanshi
- License: mit
- Created: 2020-05-19T14:13:39.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-08T10:00:11.000Z (about 2 years ago)
- Last Synced: 2024-07-29T05:32:25.232Z (3 months ago)
- Language: Python
- Homepage:
- Size: 138 KB
- Stars: 242
- Watchers: 4
- Forks: 50
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- StarryDivineSky - yusanshi/NewsRecommendation - Fi Ark TANR (其他_推荐系统 / 网络服务_其他)
README
# News Recommendation
The repository currently includes the following models.
**Models in published papers**
| Model | Full name | Paper |
| --------- | ------------------------------------------------------------------------- | -------------------------------------------------- |
| NRMS | Neural News Recommendation with Multi-Head Self-Attention | https://www.aclweb.org/anthology/D19-1671/ |
| NAML | Neural News Recommendation with Attentive Multi-View Learning | https://arxiv.org/abs/1907.05576 |
| LSTUR | Neural News Recommendation with Long- and Short-term User Representations | https://www.aclweb.org/anthology/P19-1033.pdf |
| DKN | Deep Knowledge-Aware Network for News Recommendation | https://dl.acm.org/doi/abs/10.1145/3178876.3186175 |
| Hi-Fi Ark | Deep User Representation via High-Fidelity Archive Network | https://www.ijcai.org/Proceedings/2019/424 |
| TANR | Neural News Recommendation with Topic-Aware News Representation | https://www.aclweb.org/anthology/P19-1110.pdf |
**Experimental models**
| Model | Description |
| ----- | -------------------------------------------------------------------------------------------------- |
| Exp1 | NRMS + (Sub)category + Ensemble + Positional embedding |
## Get started
Basic setup.
```bash
git clone https://github.com/yusanshi/NewsRecommendation
cd NewsRecommendation
pip3 install -r requirements.txt
```
Download and preprocess the data.
```bash
mkdir data && cd data
# Download GloVe pre-trained word embedding
wget https://nlp.stanford.edu/data/glove.840B.300d.zip
sudo apt install unzip
unzip glove.840B.300d.zip -d glove
rm glove.840B.300d.zip
# Download MIND dataset
# By downloading the dataset, you agree to the [Microsoft Research License Terms](https://go.microsoft.com/fwlink/?LinkID=206977). For more detail about the dataset, see https://msnews.github.io/.
# Uncomment the following lines to use the MIND Large dataset (Note MIND Large test set doesn't have labels, see #11)
# wget https://mind201910small.blob.core.windows.net/release/MINDlarge_train.zip https://mind201910small.blob.core.windows.net/release/MINDlarge_dev.zip https://mind201910small.blob.core.windows.net/release/MINDlarge_test.zip
# unzip MINDlarge_train.zip -d train
# unzip MINDlarge_dev.zip -d val
# unzip MINDlarge_test.zip -d test
# rm MINDlarge_*.zip
# Uncomment the following lines to use the MIND Small dataset (Note MIND Small doesn't have a test set, so we just copy the validation set as test set :)
wget https://mind201910small.blob.core.windows.net/release/MINDsmall_train.zip https://mind201910small.blob.core.windows.net/release/MINDsmall_dev.zip
unzip MINDsmall_train.zip -d train
unzip MINDsmall_dev.zip -d val
cp -r val test # MIND Small has no test set :)
rm MINDsmall_*.zip
# Preprocess data into appropriate format
cd ..
python3 src/data_preprocess.py
# Remember you shoud modify `num_*` in `src/config.py` by the output of `src/data_preprocess.py`
```
Modify `src/config.py` to select target model. The configuration file is organized into general part (which is applied to all models) and model-specific part (that some models not have).
```bash
vim src/config.py
```
Run.
```bash
# Train and save checkpoint into `checkpoint/{model_name}/` directory
python3 src/train.py
# Load latest checkpoint and evaluate on the test set
python3 src/evaluate.py
```
You can visualize metrics with TensorBoard.
```bash
tensorboard --logdir=runs
# or
tensorboard --logdir=runs/{model_name}
# for a specific model
```
> Tip: by adding `REMARK` environment variable, you can make the runs name in TensorBoard more meaningful. For example, `REMARK=num-filters-300-window-size-5 python3 src/train.py`.
## Results
| Model | AUC | MRR | nDCG@5 | nDCG@10 | Remark |
| --------- | --- | --- | ------ | ------- | ------ |
| NRMS | | | | | |
| NAML | | | | | |
| LSTUR | | | | | |
| DKN | | | | | |
| Hi-Fi Ark | | | | | |
| TANR | | | | | |
Checkpoints:
You can verify the results by simply downloading them and running `MODEL_NAME=XXXX python3 src/evaluate.py`.
## Credits
- Dataset by **MI**crosoft **N**ews **D**ataset (MIND), see .