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 # TRD
 This is the code for a Top-aware Recommender Distillation framework - TRD with Deep Reinforcement Learning.  The TRD can absorb the essence of state-of-the-art recommenders to further improve the performance of recommendations at top positions.
 
+ ## Pre-requisits
+
+### Required environment
+
+- Python 3.6
+- Torch (>=1.1.0)
+- Numpy (>=1.18.0)
+- Pandas (>=0.24.0)
+
+### Datasets
+
+- [MovieLens -100K](https://grouplens.org/datasets/movielens/100k/)
+
+- [MovieLens -1M](https://grouplens.org/datasets/movielens/1m/)
+
+- [Amazon-Cloth](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Clothing_Shoes_and_Jewelry_5.json.gz)
+
+## Modules of TRD
+
+
+
+## Example to run the codes
+
+For clarify, we use MovieLens-100k dataset as a example and treat the BPRMF method as the teacher model in the TRD framework.
+
+1. Firstly, we need install the dependent extensions.
+
+   ```python
+   python setup.py build_ext --inplace
+   ```
+
+2. Then we  run the code to load the dataset and produce the experiment data. If you want to use other datasets, you can modify the code in `data_generator.py`
+
+   ```python
+   python data_generator.py
+   ```
+
+3.  Next,  we run the code to get the results of the teacher model.
+
+   ```python
+   python run_pair_mf_train.py --dataset=ml-100k --prepro=origin 
+   ```
+
+   More details of arguments are available in help message : `python run_pair_mf_train.py --help`
+
+4. Finally, we train the student model and produce the refined recommendation lists on test set.
+
+   ```python
+   python run_trd.py --dataset=ml-100k --prepro=origin --method=bprmf --n_actions=20 --pred_score=0
+   ```
+
+   More details of arguments are available in help message : `python run_trd.py --help`
+
+## Acknowledgements
+
+We refer to the following repositories to improve our code:
+
+- state-of-the-art recommendation algorithms  with [daisyRec](https://github.com/AmazingDD/daisyRec)
+- DDPG part with [RL_DDPG_Recommendation](https://github.com/bcsrn/RL_DDPG_Recommendation)
+