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# Multi-Task Recommendations with Reinforcement Learning Source 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 baselines python3 SLmain.py --model_name=esmm ## RMTL python3 RLmain.py python3 SLmain.py --model_name=esmm --polish=1 ## Result： 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 # 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} } ```