PyTorch implementation of Off-policy Learning in Two-stage Recommender Systems

Related tags

Deep Learning Off-Policy-2-Stage
Overview

Off-Policy-2-Stage

This repo provides a PyTorch implementation of the MovieLens experiments for the following paper:

Off-policy Learning in Two-stage Recommender Systems

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi. TheWebConf (WWW) 2020.

Requirements

See environment.yml. Run conda op2s_env create -f environment.yml to install the required packages.

Run the code

Example: python run.py --loss_type loss_2s.

The "Cross-Entropy", "1-IPS", and "2-IPS" objectives respectively correspond to "loss_ce", "loss_ips", and "loss_2s" in the code.

The MovieLens-1M dataset can be found on the GroupLens website.

Cite

@inproceedings{ma2020off,
  title={Off-policy Learning in Two-stage Recommender Systems},
  author={Ma, Jiaqi and Zhao, Zhe and Yi, Xinyang and Yang, Ji and Chen, Minmin and Tang, Jiaxi and Hong, Lichan and Chi, Ed H},
  booktitle={Proceedings of The Web Conference 2020},
  pages={463--473},
  year={2020}
}
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Comments
  • Replication of the experiments

    Replication of the experiments

    I'm trying to replicate the results obtained in the paper using the code in the repository and I have some questions about:

    • How can I set the sample size, c1 and c2 parameters?
    • Is the default dataset split the same used for the experiments in the paper? Or How can I set it in the same way?
    • How can I include the Wiki10 dataset?
    • Is the seed used in your experiments the same reported in the code? (i.e. 0)
    opened by CavenaghiEmanuele 0
Owner
Jiaqi Ma
Jiaqi Ma
GitHub
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