# RecSim: A Configurable Simulation Platform for Recommender Systems

arXiv: https://arxiv.org/abs/1909.04847 · HTML: https://mlsanity.com/paper/1909.04847/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 30 of 94)

- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems](https://mlsanity.com/paper/2310.09233/) (2023)
- [On Generative Agents in Recommendation](https://mlsanity.com/paper/2310.10108/) (2023)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [LLM-Powered User Simulator for Recommender System](https://mlsanity.com/paper/2412.16984/) (2024)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023)
- [Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2503.16734/) (2025)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions](https://mlsanity.com/paper/2109.03540/) (2021)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation](https://mlsanity.com/paper/2109.08331/) (2021)
- [On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning](https://mlsanity.com/paper/2110.06539/) (2021)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders](https://mlsanity.com/paper/2602.16938/) (2026)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems](https://mlsanity.com/paper/2406.01631/) (2024)
- [YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models](https://mlsanity.com/paper/2505.07581/) (2025)
- [Variation Control and Evaluation for Generative SlateRecommendations](https://mlsanity.com/paper/2102.13302/) (2021)
- [Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities](https://mlsanity.com/paper/2105.02377/) (2021)
- [EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems](https://mlsanity.com/paper/2402.15164/) (2024)
- [Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces](https://mlsanity.com/paper/2604.08362/) (2026)
- [Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2302.03561/) (2023)
- [Modeling Earth-Scale Human-Like Societies with One Billion Agents](https://mlsanity.com/paper/2506.12078/) (2025)

## Abstract (arXiv)

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.