HyperTeNet (IEEE ICDM, 2021)

Hypergraph and Transformer-based Neural Network for Personalized List Continuation (arXiv)

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Dependencies

Download the code from this anonymized link and make sure that the environment is CUDA-enabled. The code works on Python 3.5+.

To install the dependencies, use:

pip install requirements.txt

If the requirements installation fails for some reason, you can use the following scripts to install the PyTorch components individually:

pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
pip install --no-index torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-sparse==0.6.1 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-cluster==1.5.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install torch-geometric==1.4.2 -q

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Extract Dataset

To train or evaluate HyperTeNet on a dataset, extract the zipped dataset in the data directory. The datasets can be downloaded from here.

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Training and Evaluation

Use the following scripts to train/evaluate the model. For evaluation, make sure that a trained model corresponding to a dataset is present in the saved_models directory.

1. Art of the Mix (AOTM)

Train: python train.py --path="./data/aotm/" --dataset="aotm" --num_layers=2 --num_epochs=300 --num_negatives=5 --num_negatives_seq=2 --lr=0.001

Evaluate: python eval.py --path="./data/aotm/" --dataset="aotm" --num_layers=2

2. Goodreads

Train: python train.py --path="./data/goodreads/" --dataset="goodreads" --num_layers=2 --num_epochs=300 --num_negatives=3 --num_negatives_seq=4 --lr=0.0007

Evaluate: python eval.py --path="./data/goodreads/" --dataset="goodreads" --num_layers=2

3. Spotify

Train: python train.py --path="./data/spotify/" --dataset="spotify" --num_layers=3 --num_epochs=300 --num_negatives=3 --num_negatives_seq=5 --lr=0.0008

Evaluate: python eval.py --path="./data/spotify/" --dataset="spotify" --num_layers=3

4. Zhihu

Train: python train.py --path="./data/zhihu/" --dataset="zhihu" --num_layers=3 --num_epochs=300 --num_negatives=5 --num_negatives_seq=6 --lr=0.002

Evaluate: python eval.py --path="./data/zhihu/" --dataset="zhihu" --num_layers=3

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Results

Evaluating the trained models present in the saved_models directory using the above commands can be used to get results similar to the ones reported in the following table:

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