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RecSim: A Configurable Simulation Platform for Recommender Systems

arXiv 1909.04847 ↗First posted 11 Sep 2019cs.LG cs.HC cs.IRcited by 91

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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.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 13; 2023: 16; 2024: 19; 2025: 14; 2026: 12.

91 papers.

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  1. 2023User Behavior Simulation with Large Language Model based Agentscited by 136
  2. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  3. 2023On Generative Agents in Recommendationcited by 88
  4. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  5. 2024PersonalLLM: Tailoring LLMs to Individual Preferencescited by 56
  6. 2024LLM-Powered User Simulator for Recommender Systemcited by 38
  7. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  8. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  9. 2023Multi-Task Recommendations with Reinforcement Learningcited by 26
  10. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Modelscited by 25
  11. 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
  12. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
38 more
  1. 2021Measuring Recommender System Effects with Simulated Userscited by 20
  2. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 19
  3. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  4. 2021Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulationcited by 16
  5. 2021On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learningcited by 14
  6. 2026A Survey on LLM-based Conversational User Simulationcited by 14
  7. 2023On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendationcited by 13
  8. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  9. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  10. 2024SUBER: An RL Environment with Simulated Human Behavior for Recommender Systemscited by 11
  11. 2025YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Modelscited by 11
  12. 2021Variation Control and Evaluation for Generative SlateRecommendationscited by 10
  13. 2026ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenderscited by 10
  14. 2021Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilitiescited by 9
  15. 2023Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspectivecited by 8
  16. 2024EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systemscited by 8
  17. 2025Modeling Earth-Scale Human-Like Societies with One Billion Agentscited by 8
  18. 2021Simulations in Recommender Systems: An industry perspectivecited by 6
  19. 2022Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysiscited by 6
  20. 2025Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Actioncited by 6
  21. 2026Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Tracescited by 6
  22. 2021RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender Systemcited by 5
  23. 2022Synthetic Data-Based Simulators for Recommender Systems: A Surveycited by 5
  24. 2023Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systemscited by 5
  25. 2023AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systemscited by 5
  26. 2023RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluationcited by 5
  27. 2024Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Datacited by 5
  28. 2021Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulationcited by 4
  29. 2023Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudgingcited by 4
  30. 2024Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotionscited by 4
  31. 2024Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Frameworkcited by 4
  32. 2024Active Inference and Human--Computer Interactioncited by 4
  33. 2025InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agentscited by 4
  34. 2025Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systemscited by 4
  35. 2021Imitate TheWorld: A Search Engine Simulation Platformcited by 3
  36. 2022Learning to Rank For Push Notifications Using Pairwise Expected Regretcited by 3
  37. 2022Examining Policy Entropy of Reinforcement Learning Agents for Personalization Taskscited by 3
  38. 2022Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagementcited by 3

50 most cited of 91 All 91 (JSON)