olivierjeunen / dual-bandit-kdd-2020

Source code for our paper "Joint Policy-Value Learning for Recommendation" published at KDD 2020.

Joint Policy-Value Learning for Recommendation

Source code for our paper "Joint Policy-Value Learning for Recommendation" published at KDD 2020.

Reproducibility

To generate a virtual Python environment that holds all the packages our work relies on, run:

virtualenv -p python3 dual_bandit
source dual_bandit/bin/activate
pip3 install -r requirements.txt

Now, you can run any of the three ''RunABTest'' files to run all experimental results corresponding to a column in Fig. 2 of the paper. For example:

python3 src/RunABTest_Uniform.py

Acknowledgements

We're grateful to Hao-Jun Michael Shi (Northwestern University) and Dheevatsa Mudigere (Facebook) for their implementation of L-BFGS in PyTorch: https://github.com/hjmshi/PyTorch-LBFGS.

Paper

If you use our code in your research, please remember to cite our paper:

    @inproceedings{JeunenKDD2020,
      author = {Jeunen, Olivier and Rohde, David and Vasile, Flavian and Bompaire, Martin},
      title = {Joint Policy-Value Learning for Recommendation},
      booktitle = {Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
      series = {KDD '20},
      year = {2020},
      publisher = {ACM},
    }