News Recommendation

Presentation

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/

Get started to train

Basic setup.

git clone https://github.com/yusanshi/NewsRecommendation
cd NewsRecommendation
pip3 install -r requirements.txt

Download and preprocess the data.

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).

vim src/config.py

Run.

# 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.

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.

Optim study in MIND-mini

Model	AUC	MRR	nDCG@5	nDCG@10	Remark
baseline	0.6253	0.2823	0.3051	0.3731
+SGD	0.5188	0.2148	0.2250	0.2905
+AdamW	0.6298	0.2841	0.3091	0.3765

Norm study in MIND-mini

Model	AUC	MRR	nDCG@5	nDCG@10	Remark
baseline	0.6253	0.2823	0.3051	0.3731
+BN	0.5252	0.2476	0.2565	0.3181
+GN	0.6323	0.2884	0.3122	0.3795
+IN	0.6321	0.2847	0.3101	0.3785
+LN	0.6404	0.2905	0.3172	0.3835

Results in MIND-mini

Model	AUC	MRR	nDCG@5	nDCG@10	Remark
baseline	0.6253	0.2823	0.3051	0.3731
+LN +AdamW + Cosine decay	0.6421	0.2960	0.3239	0.3890

Get started to open website

cd ..
python3 src/web.py

Acknowledge

@misc{yusanshi2020news-recommendation,
  title={news-recommendation},
  author={yusanshi},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished={\url{https://github.com/yusanshi/news-recommendation}},
  year={2020}
}

Citation

@misc{Maguire2022news-recommendation,
  title={news-recommendation-system},
  author={Maguire},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished={\url{https://github.com/Maguire1999/NewsRecommendationSystem}},
  year={2022}
}