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 |
Basic setup.
git clone https://github.com/yusanshi/NewsRecommendation
cd NewsRecommendation
pip3 install -r requirements.txtDownload 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.pyRun.
# 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.pyYou can visualize metrics with TensorBoard.
tensorboard --logdir=runs
# or
tensorboard --logdir=runs/{model_name}
# for a specific modelTip: by adding
REMARKenvironment variable, you can make the runs name in TensorBoard more meaningful. For example,REMARK=num-filters-300-window-size-5 python3 src/train.py.
| Model | AUC | MRR | nDCG@5 | nDCG@10 | Remark |
|---|---|---|---|---|---|
| NRMS | |||||
| NAML | |||||
| LSTUR | |||||
| DKN | |||||
| Hi-Fi Ark | |||||
| TANR |
Checkpoints: https://drive.google.com/open?id=TODO
You can verify the results by simply downloading them and running MODEL_NAME=XXXX python3 src/evaluate.py.