karthik446 / newsrecommendation

Implementations of some methods in news recommendation.

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
Exp2	RoBERTa (fixed or fine-tuned, word level or sentence level) + (Sub)category + 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/.
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

# 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
# This will also generate prediction file `data/test/prediction.txt`
python3 src/evaluate.py

# or

chmod +x run.sh
./run.sh

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	nMRR	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

• Dataset by MIcrosoft News Dataset (MIND), see https://msnews.github.io/.