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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.

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.

Results

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.

Credits