“基于强化学习的推荐系统的生成对抗用户模型”的Tensorflow实现_Python_Shell_下载.zip

# Generative Adversarial User Model Tensorflow implementation for: [Generative Adversarial User Model for Reinforcement Learning Based Recommendation System](http://proceedings.mlr.press/v97/chen19f/chen19f.pdf) [1] (Currently the ant financial dataset is not authorized to released. Experiments on other public dataset are released.) ## Setup ### Install Clone and install the current package. ``` pip install -e . ``` ### Data The dataset can be obtained via the [shared dropbox folder](https://www.dropbox.com/sh/57gqb1c98gxasr8/AABDPPVnggypWwn2NsLNq7x6a?dl=0). After downloading the `.txt` files in the shared folder, put then under the 'dropbox' folder, so that the default bash script can automatically find them. Finally the project has the following folder structure: ``` ganrl |___ganrl # source code | |___common # common implementations | |___experiment_user_model # code for experiments in Sec 6.1 in the paper | |___dropbox # yelp, tb, rsc dataset. |___process_data.py |___process_data.sh |___yelp.txt |___tb.txt |...... ... ``` Process the data before running the experiments: ``` cd dropbox ./process_data.sh ``` Explanation of the original `.txt` file: The column 'session_new_index' corresponds to user ID. The column 'item_new_index' corresponds to item ID. If several items have the same 'Time' index, then they are displayed at the same time (in the same display set). ## Experiments By modifying the sh scripts, You can tune the hyperparameters like the architecture of the neural networks, learning rate, etc. ### GA User Model with Shannon Entropy Navigate to the experiment folder. You can run the sh script directly or set the hyperparameters by yourself. To try a different split of train, test, validation sets, you can change `-resplit False` to `-resplit True` in the sh file. ``` cd ganrl/experiment_user_model/ ./run_gan_user_model.sh ``` The trained model will be saved in `scratch/` folder. ### GA User Model with L2 Regularization First, train the user model using Shannon Entropy by running `./run_gan_user_model.sh`. With this saved model as an initilization, you can continue to train the model using other regularizations. For example, L2: ``` cd ganrl/experiment_user_model/ ./run_gan_user_model.sh ./run_gan_L2_regularized_yelp.sh ``` ## Citation If you found it useful in your research, please consider citing ``` @inproceedings{chen2019generative, title={Generative Adversarial User Model for Reinforcement Learning Based Recommendation System}, author={Chen, Xinshi and Li, Shuang and Li, Hui and Jiang, Shaohua and Qi, Yuan and Song, Le}, booktitle={International Conference on Machine Learning}, pages={1052--1061}, year={2019} } ``` ## References [1] Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song. "Generative Adversarial User Model for Reinforcement Learning Based Recommendation System." *In International Conference on Machine Learning.* 2019.