Deep Reinforcement Learning based Recommender System in Tensorflow

The implemetation of Deep Reinforcement Learning based Recommender System from the paper Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling by Liu et al. Build recommender system with DDPG algorithm. Add state representation module to produce trainable state for RL algorithm from data. This is not the official implementation of the paper.

Dataset

unzip ./ml-1m.zip

Trying to improve performance of RL based recommender system. The report contains the result of Using the actor network with embedding layer, reducing overestimated Q value, using several pretrained embedding and applying PER.
Making new embedding files. Previous one contains the information for entire timelines which can mislead model.
Updating Train and Evaluation part. Currently, I didn't follow the exact same training and evaluation procedure in the paper.

for evalutation data
- precision@5 : 0.479, ndcg@5 : 0.471
- precision@10 : 0.444, ndcg@10 : 0.429

The saved model of actor and critic are generated after the training is done.
```
python train.py
```
Evalutation
Make sure there exist the saved models in the right directory
Run jupyter notebook and check "evaluation.ipynb"

tensorflow==2.5.0
scikit-learn==0.23.2
matplotlib==3.3.3