mlsanityData to 7 Oct 2026

EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems

arXiv 2402.15164 ↗First posted 23 Feb 2024cs.IR cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 38 of its 62 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

38 of 62 references.

References · 38

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26 more
  1. 2020Critic Regularized Regressioncited by 269
  2. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 246
  3. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  4. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 181
  5. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  6. 2018A Simple Convolutional Generative Network for Next Item Recommendationcited by 120
  7. 2019Benchmarking Batch Deep Reinforcement Learning Algorithmscited by 108
  8. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 99
  9. 2022KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systemscited by 94
  10. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 91
  11. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 89
  12. 2018Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learningcited by 80
  13. 2020Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluationcited by 77
  14. 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
  15. 2022KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videoscited by 69
  16. 2021AutoDebias: Learning to Debias for Recommendationcited by 68
  17. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  18. 2020Self-Supervised Reinforcement Learning for Recommender Systemscited by 49
  19. 2023Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendationcited by 38
  20. 2023KuaiSim: A Comprehensive Simulator for Recommender Systemscited by 30
  21. 2022Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systemscited by 22
  22. 2022PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagementcited by 21
  23. 2023Exploration and Regularization of the Latent Action Space in Recommendationcited by 17
  24. 2023Two-Stage Constrained Actor-Critic for Short Video Recommendationcited by 16
  25. 2023Reinforcing User Retention in a Billion Scale Short Video Recommender Systemcited by 15
  26. 2023Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systemscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2025Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendationcited by 2
  2. 2025DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Rewardcited by 2
  3. 2025Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendationcited by 1
  4. 2026LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendationcited by 1
  5. 2025Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systemscited by 0
  6. 2026Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendationcited by 0
  7. 2026When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPscited by 0
  8. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 0