Official implementation of our paper HAMUR: Hyper Adapter for Multi-Domain Recommendation in CIKM 2023.

You could cite our paper if you find this repository interesting or helpful:

@inproceedings{li2023hamur,
  title={HAMUR: Hyper Adapter for Multi-Domain Recommendation},
  author={Li, Xiaopeng and Yan, Fan and Zhao, Xiangyu and Wang, Yichao and Chen, Bo and Guo, Huifeng and Tang, Ruiming},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages={1268--1277},
  year={2023}
}

Source code of HAMUR: Hyper Adapter for Multi-Domain Recommendation, in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management(CIKM 23').

• torch >=1.7.0
• numpy >=1.23.5
• pandas >=1.5.3
• scikit-learn >=0.23.2

In this paper, we use two datasets, Aliccp and movieLens. Dataset samples are shown in example/ranking/data.

Real Dataset download methods:

• Aliccp : Dowload address https://tianchi.aliyun.com/dataset/408.
• Movielens : The raw data precess file could be found in Torch-Rechub-ml-1m. You coud direclt dowload precessed file from https://cowtransfer.com/s/5a3ab69ebd314e.

In this repo, we offer the following models, list as follows. And their suructure are shown in the picture.

• Pure MLP as Multi-domain Backbone models.
• MLP + HAMUR
• Pure Wide & Deep as Multi-domain Backbone models.
• Wide & Deep + HUMUR
• Pure DCN as Multi-domain Backbone models.
• DCN + HUMUR

git clone https://github.com/Applied-Machine-Learning-Lab/HAMUR.git

cd examples
# For Aliccp
python run_ali_ccp_ctr_ranking_multi_domain.py --model_name mlp_adp --epoch 200 --device cpu --seed 2022 
# For MovieLens
python run_movielens_rank_multi_domain.py --model_name mlp_adp --epoch 200 --device cpu --seed 2022 

Our code is developed based on Torch-RecHub. Thanks to their contribution.