# KuaiSim: A Comprehensive Simulator for Recommender Systems

arXiv: https://arxiv.org/abs/2309.12645 · HTML: https://mlsanity.com/paper/2309.12645/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 30 of 30)

- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation](https://mlsanity.com/paper/2504.12722/) (2025)
- [RecoWorld: Building Simulated Environments for Agentic Recommender Systems](https://mlsanity.com/paper/2509.10397/) (2025)
- [Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM](https://mlsanity.com/paper/2312.15450/) (2023)
- [SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems](https://mlsanity.com/paper/2406.01631/) (2024)
- [SIGMA: Selective Gated Mamba for Sequential Recommendation](https://mlsanity.com/paper/2408.11451/) (2024)
- [The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit](https://mlsanity.com/paper/2501.02173/) (2025)
- [EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems](https://mlsanity.com/paper/2402.15164/) (2024)
- [AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems](https://mlsanity.com/paper/2310.03984/) (2023)
- [Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants](https://mlsanity.com/paper/2410.12891/) (2024)
- [Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://mlsanity.com/paper/2508.18142/) (2025)
- [Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing](https://mlsanity.com/paper/2601.04554/) (2026)
- [Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems](https://mlsanity.com/paper/2403.17634/) (2024)
- [GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation](https://mlsanity.com/paper/2505.21154/) (2025)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)
- [Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://mlsanity.com/paper/2604.09549/) (2026)
- [G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation](https://mlsanity.com/paper/2508.05709/) (2025)
- [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025)
- [Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation](https://mlsanity.com/paper/2310.16566/) (2023)
- [Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data](https://mlsanity.com/paper/2511.04541/) (2025)
- [LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation](https://mlsanity.com/paper/2601.19585/) (2026)
- [Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems](https://mlsanity.com/paper/2603.11399/) (2026)
- [MultiScale Contextual Bandits for Long Term Objectives](https://mlsanity.com/paper/2503.17674/) (2025)
- [Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation](https://mlsanity.com/paper/2507.18756/) (2025)
- [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings](https://mlsanity.com/paper/2510.04239/) (2025)
- [Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation](https://mlsanity.com/paper/2603.03820/) (2026)
- [RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents](https://mlsanity.com/paper/2605.18805/) (2026)
- [Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization](https://mlsanity.com/paper/2605.28810/) (2026)
- [Learning Collective Dynamics with Differentiable Gaussian Representations](https://mlsanity.com/paper/2609.28405/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-term user rewards. However, deploying RL models directly in online environments and generating authentic data through A/B tests can pose challenges and require substantial resources. Simulators offer an alternative approach by providing training and evaluation environments for RS models, reducing reliance on real-world data. Existing simulators have shown promising results but also have limitations such as simplified user feedback, lacking consistency with real-world data, the challenge of simulator evaluation, and difficulties in migration and expansion across RSs. To address these challenges, we propose KuaiSim, a comprehensive user environment that provides user feedback with multi-behavior and cross-session responses. The resulting simulator can support three levels of recommendation problems: the request level list-wise recommendation task, the whole-session level sequential recommendation task, and the cross-session level retention optimization task. For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research. We also restructure existing competitive simulators on the KuaiRand Dataset and compare them against KuaiSim to future assess their performance and behavioral differences. Furthermore, to showcase KuaiSim's flexibility in accommodating different datasets, we demonstrate its versatility and robustness when deploying it on the ML-1m dataset.