无标题代码代码代码代码代码

资源文件列表(大概)

文件名

大小

RMTL-main/

RMTL-main/Framework.pdf

27.27KB

RMTL-main/README.md

2.16KB

RMTL-main/RLmain.py

1.4KB

RMTL-main/SLmain.py

7.99KB

RMTL-main/agents/

RMTL-main/agents/DDPG_ESMM.py

12.81KB

RMTL-main/agents/DDPG_ESMM_BC.py

7.81KB

RMTL-main/agents/ReplayBuffer.py

1.72KB

RMTL-main/agents/__pycache__/

RMTL-main/agents/__pycache__/DDPG_ESMM.cpython-38.pyc

8.87KB

RMTL-main/agents/__pycache__/DDPG_ESMM_BC.cpython-38.pyc

5.17KB

RMTL-main/agents/__pycache__/ReplayBuffer.cpython-38.pyc

1.76KB

RMTL-main/doc.md

8.15KB

RMTL-main/env.py

6.12KB

RMTL-main/layers/

RMTL-main/layers/__pycache__/

RMTL-main/layers/__pycache__/critic.cpython-38.pyc

2.04KB

RMTL-main/layers/__pycache__/layers.cpython-38.pyc

1.77KB

RMTL-main/layers/critic.py

2.39KB

RMTL-main/layers/esmm.py

1.7KB

RMTL-main/layers/layers.py

1.3KB

RMTL-main/pretrain.zip

10.5MB

RMTL-main/slmodels/

RMTL-main/slmodels/__pycache__/

RMTL-main/slmodels/__pycache__/aitm.cpython-38.pyc

2.94KB

RMTL-main/slmodels/__pycache__/esmm.cpython-38.pyc

1.97KB

RMTL-main/slmodels/__pycache__/layers.cpython-38.pyc

1.77KB

RMTL-main/slmodels/__pycache__/mmoe.cpython-38.pyc

2.87KB

RMTL-main/slmodels/__pycache__/omoe.cpython-38.pyc

2.58KB

RMTL-main/slmodels/__pycache__/ple.cpython-38.pyc

3.62KB

RMTL-main/slmodels/__pycache__/sharedbottom.cpython-38.pyc

1.92KB

RMTL-main/slmodels/__pycache__/singletask.cpython-38.pyc

2.22KB

RMTL-main/slmodels/aitm.py

2.5KB

RMTL-main/slmodels/esmm.py

1.7KB

RMTL-main/slmodels/layers.py

1.3KB

RMTL-main/slmodels/metaheac.py

7.94KB

RMTL-main/slmodels/mmoe.py

2.06KB

RMTL-main/slmodels/omoe.py

2.05KB

RMTL-main/slmodels/ple.py

3.8KB

RMTL-main/slmodels/sharedbottom.py

1.43KB

RMTL-main/slmodels/singletask.py

1.63KB

RMTL-main/train/

RMTL-main/train/Arguments.py

1.54KB

RMTL-main/train/__pycache__/

RMTL-main/train/__pycache__/Arguments.cpython-38.pyc

1.42KB

RMTL-main/train/__pycache__/run.cpython-38.pyc

7.96KB

RMTL-main/train/__pycache__/utils.cpython-38.pyc

7.78KB

RMTL-main/train/run.py

11.62KB

RMTL-main/train/utils.py

9.34KB

资源内容介绍

无标题代码代码代码代码代码

# Multi-Task Recommendations with Reinforcement LearningSource code of [Multi-Task Recommendations with Reinforcement Learning](https://dl.acm.org/doi/10.1145/3543507.3583467)Code for RetailRocket Dataset.**Google Drive link for processed RetailRocket data:** https://drive.google.com/file/d/1THRWKttdpmcNaEc1DtKwxgYlV8RLMtV5/view?usp=sharing# Model Code+ 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# How to run it## MTL baselinespython3 SLmain.py --model_name=esmm## RMTLpython3 RLmain.pypython3 SLmain.py --model_name=esmm --polish=1## Result：test: best auc: 0.732444172986328100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134/134 [00:07<00:00, 19.14it/s]task 0, AUC 0.7273702846096346, Log-loss 0.20675417715656488task 1, AUC 0.7247954179346048, Log-loss 0.048957254763240504 # Citation：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}}```

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