EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
arxiv(2024)
摘要
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.
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