Source code of Multi-Task Recommendations with Reinforcement Learning

Code for RetailRocket Dataset.

Google Drive link for processed RetailRocket data: https://drive.google.com/file/d/1THRWKttdpmcNaEc1DtKwxgYlV8RLMtV5/view?usp=sharing

• layers: stores common network structures

  • critic: critic network
  • esmm: esmm(actor) network, can introduce other MTL models as actor inside slmodels
  • layers: classical Embedding layers and MLP layers

• slmodels: SL baseline models

• agents: RL models

• train: training-related configuration

• env.py: offline sampling simulation environment

• RLmain.py: main RL training program

• SLmain.py: SL training main program

• dataset

  • rtrl：retrailrocket dataset（Convert to MDP format：）[timestamp,sessionid,itemid,pay,click], [itemid,feature1,feature2,..],6:2:2

python3 SLmain.py --model_name=esmm

python3 RLmain.py python3 SLmain.py --model_name=esmm --polish=1

test: best auc: 0.732444172986328 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134/134 [00:07<00:00, 19.14it/s] task 0, AUC 0.7273702846096346, Log-loss 0.20675417715656488 task 1, AUC 0.7247954179346048, Log-loss 0.048957254763240504

Please cite with the below bibTex if you find it helpful to your research.

@inproceedings{liu2023multi,
  title={Multi-Task Recommendations with Reinforcement Learning},
  author={Liu, Ziru and Tian, Jiejie and Cai, Qingpeng and Zhao, Xiangyu and Gao, Jingtong and Liu, Shuchang and Chen, Dayou and He, Tonghao and Zheng, Dong and Jiang, Peng and others},
  booktitle={Proceedings of the ACM Web Conference 2023},
  pages={1273--1282},
  year={2023}
}