Controllable Multi-Objective Re-ranking with Policy Hypernetworks (SIGKDD 2023)

Controllable-Multi-Objective-Reranking is modified on LibRerank

Requirements

• Ubuntu 20.04 or later (64-bit)
• GPU support requires a CUDA®-enabled card
• For NVIDIA GPUs, the r455 driver must be installed

For wheel installation:

• Python 3.8
• pip 19.0 or later

Quick Started

Our experimental environment is Ubuntu20.04(necessary)+Python3.8(necessary)+CUDA11.4+TensorFlow1.15.5.

Create virtual environment(optional)

pip install --user virtualenv
~/.local/bin/virtualenv -p python3 ./venv
source venv/bin/activate

Install CMR from source

git clone https://github.com/lyingCS/Controllable-Multi-Objective-Reranking.git
cd Controllable-Multi-Objective-Reranking
pip config set global.extra-index-url https://pypi.ngc.nvidia.com    # optional
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple    # optional
pip config set global.trusted-host mirrors.aliyun.com\npypi.ngc.nvidia.com    # optional
make init 

Decompress evaluator checkpoint

For facilitate the training of the generator, we provide a version of the checkpoints of CMR_evaluator that have been pretrained. We first need to decompress it.

tar -xzvf ./model/save_model_ad/10/*.tar.gz -C ./model/save_model_ad/10/

Run example

Run re-ranker

python run_reranker.py

Model parameters can be set by using a config file, and specify its file path at --setting_path, e.g., python run_ranker.py --setting_path config. The config files for the different models can be found in example/config. Moreover, model parameters can also be directly set from the command line.

For more information please refer to LibRerank_README.md

Citation

If you find our code or paper useful, please cite the paper:

@inproceedings{chen2023controllable,
  title={Controllable Multi-Objective Re-ranking with Policy Hypernetworks},
  author={Chen, Sirui and Wang, Yuan and Wen, Zijing and Li, Zhiyu and Zhang, Changshuo and Zhang, Xiao and Lin, Quan and Zhu, Cheng and Xu, Jun},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={3855--3864},
  year={2023}
}