Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use.
@article{arxiv.2402.15164,
title = {EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems},
author = {Yuanqing Yu and Chongming Gao and Jiawei Chen and Heng Tang and Yuefeng Sun and Qian Chen and Weizhi Ma and Min Zhang},
journal= {arXiv preprint arXiv:2402.15164},
year = {2024}
}