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

• MovieLens -1M

• Amazon-Cloth

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 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 data_generator.py

3. Next, we run the code to get the results of the teacher model. More details of arguments are available in help message : python run_pair_mf_train.py --help

python run_pair_mf_train.py --dataset=ml-100k --prepro=origin 

4. Finally, we train the student model and produce the refined recommendation lists on test set. More details of arguments are available in help message : python run_trd.py --help

   python run_trd.py --dataset=ml-100k --prepro=origin --method=bprmf --n_actions=20 --pred_score=0

Acknowledgements

We refer to the following repositories to improve our code:

• state-of-the-art recommendation algorithms with daisyRec
• DDPG part with RL_DDPG_Recommendation