EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems

arXiv:2402.15164v1 Announce Type: cross
Abstract: Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly recognized for their ability to improve long-term user engagement. Yet, the field grapples with challenges such as the absence of accessible frameworks, inconsistent evaluation standards, and the complexity of replicating prior work. Addressing these obstacles, we present EasyRL4Rec, a user-friendly and efficient library tailored for RL-based RSs. EasyRL4Rec features lightweight, diverse RL environments built on five widely-used public datasets, and is equipped with comprehensive core modules that …

arxiv code cs.ir cs.lg library recommender systems reinforcement reinforcement learning systems type