Combating Heterogeneous Model Biases in Recommendations via Boosting

Basic Usage

• Change the experimental settings in main_config.cfg and the model hyperparameters in model_config.
  
• Run main.py to train and test models.
  
• Command line arguments are also acceptable with the same naming in configuration files. (Both main/model config)

For example: python main.py --model_name MultVAE --lr 0.001

Running LOCA

Before running LOCA, you need (1) user embeddings to find local communities and (2) the global model to cover users who are not considered by local models.

1. Run a single MultVAE to get user embedding vectors and the global model:

python main.py --model_name MultVAE

2. Train LOCA with the specific backbone model:

python main.py --model_name LOCA_VAE

Running CFBoost and CFAdaboost

Change different designs of α, design1 and design2 in the code.

python main.py --model_name MF_adaboost

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Requirements

• Python 3.7 or higher
• Torch 1.5 or higher

Appendix

Complete Appendix can be found here

Citation

cited papaer:

@inproceedings{10.1145/3701551.3703505,
author = {Pan, Jinhao and Caverlee, James and Zhu, Ziwei},
title = {Combating Heterogeneous Model Biases in Recommendations via Boosting},
year = {2025},
isbn = {9798400713293},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3701551.3703505},
doi = {10.1145/3701551.3703505},
booktitle = {Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining},
pages = {222–231},
numpages = {10},
keywords = {boosting, collaborative filtering, model biases, recommender systems},
location = {Hannover, Germany},
series = {WSDM '25}
}