Vectorization

News: Our codes have been merged into ULTRA framework (https://github.com/ULTR-Community/ULTRA).

This project is based on ULTRA (https://github.com/ULTR-Community/ULTRA). Please see its document for more details.

Our main code is in ultra/learning_algorithm/vectorization.py.

load dataset

Please download datasets first, then run the following command:

bash ./example/Yahoo/offline_exp_pipeline.sh
bash ./example/Istella-S/offline_exp_pipeline.sh

run experiment

You can modify "dimension=xxx" in the file of ./config/xxx.json, to modify the dimension of Vectorization

• Real click on Yahoo!

python3 main.py \
    --max_train_iteration=15000 \
    --data_dir=./Yahoo_letor/tmp_data/ \
    --model_dir=./tmp/model/ \
    --output_dir=./tmp/model/output/ \
    --setting_file=./config/vector_tiangong.json

• Trust bias on Yahoo!

python3 main.py \
    --max_train_iteration=15000 \
    --data_dir=./Yahoo_letor/tmp_data/ \
    --model_dir=./tmp/model/ \
    --output_dir=./tmp/model/output/ \
    --setting_file=./config/vector_trust.json

• Real click on Istella-S

python3 main.py \
    --max_train_iteration=30000 \
    --steps_per_checkpoint=200 \
    --data_dir=./istella-s-letor/tmp_data/ \
    --model_dir=./tmp/model/ \
    --output_dir=./tmp/model/output/ \
    --setting_file=./config/vector_tiangong.json 

• Trust bias on Istella-S

python3 main.py \
    --max_train_iteration=30000 \
    --steps_per_checkpoint=200 \
    --data_dir=./istella-s-letor/tmp_data/ \
    --model_dir=./tmp/model/ \
    --output_dir=./tmp/model/output/ \
    --setting_file=./config/vector_trust.json

Citation

Please consider citing the following paper when using our code for your application.

@inproceedings{chen2022scalar,
  title={Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank},
  author={Mouxiang Chen and Chenghao Liu and Zemin Liu and Jianling Sun},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022}
}