The code to reproduce the experimental results for "A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation" (In the 24th European Conference on Artificial Intelligence, ECAI 2020).

The data pre-processing codes is also included. You could download Amazon data from here, and run the amazon.py.

The code has been tested under Windows 10 and Ubuntu 16.04 with TensorFlow 1.14.0 and Python 3.6.8.

Support independent training with CPU and joint training with CPU and GPU when CUDA is available.

You can download and add these resource to this project under the folder ./resource.

The pre-trained word vectors is available on GloVe.6B, which was trained on Wikipedia2014 and Gigaword 5.

The Long Stopword List can be obtained here.

Take Digital_Music for example. After getting the source data, you should run data process first:

python amazon.py 

To train our DDPG model on Digital_Music:

python DDPG_Rec.py 

or our DQN model on Digital_Music:

python DQN_Rec.py

You can modify the source codes to run other datasets. For MF-class methods, you should change the input by modify 'method' from 'glove' to 'mf'.

Please cite this article as:

@inproceedings{wang2020text,
	title={A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation},
	author={Chaoyang Wang and Zhiqiang Guo and Jianjun Li and Peng Pan and Guohui Li},
	booktitle={Proceedings of the 24th European Conference on Artificial Intelligence},
	pages={537--544},
	year={2020},
	doi = {10.3233/FAIA200136},
	publisher={IOS Press}
}