MBHT

This is our implementation for our paper Multi-Behavior Hypergraph-Enhanced Transformer for Next-Item Recommendation, accepted by KDD'22.

Requirements

The code is built on Pytorch and the RecBole benchmark library. Run the following code to satisfy the requeiremnts by pip:

pip install -r requirements.txt

Datasets

Download the three public datasets we use in the paper at:

https://drive.google.com/file/d/1OFT_5Xp_az-GSHIl7QEPB9zhulbooLzE/view?usp=sharing

Unzip the datasets and move them to ./dataset/

Run MBHT

python run_MBHT.py --model=[MBHT] --dataset=[tmall_beh] --gpu_id=[0] --batch_size=[2048], where [value] means the default value.

Tips

• Note that we modified the evaluation sampling setting in recbole/sampler/sampler.py to make it static.
• The model code is at recbole/model/sequential_recommender/mbht.py.
• Feel free to explore other baseline models provided by the RecBole library and directly run them to compare the performances.