# RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

arXiv: https://arxiv.org/abs/2103.08057 · HTML: https://mlsanity.com/paper/2103.08057/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 64 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 30 of 34)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Dopamine: A Research Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.06110/) (2018)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [Bayesian Layers: A Module for Neural Network Uncertainty](https://mlsanity.com/paper/1812.03973/) (2018)
- [Horizon: Facebook's Open Source Applied Reinforcement Learning Platform](https://mlsanity.com/paper/1811.00260/) (2018)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology](https://mlsanity.com/paper/1905.12767/) (2019)
- [Deep Probabilistic Programming](https://mlsanity.com/paper/1701.03757/) (2017)
- [Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1801.06176/) (2018)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [Simple, Distributed, and Accelerated Probabilistic Programming](https://mlsanity.com/paper/1811.02091/) (2018)
- [ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games](https://mlsanity.com/paper/1707.01067/) (2017)
- [Reinforcement Learning based Recommender System using Biclustering Technique](https://mlsanity.com/paper/1801.05532/) (2018)
- [From Recommendation Systems to Facility Location Games](https://mlsanity.com/paper/1809.02931/) (2018)
- [WES: Agent-based User Interaction Simulation on Real Infrastructure](https://mlsanity.com/paper/2004.05363/) (2020)

## Built on by (most cited first, 21 of 21)

- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Recommendation Fairness: From Static to Dynamic](https://mlsanity.com/paper/2109.03150/) (2021)
- [Should I send this notification? Optimizing push notifications decision making by modeling the future](https://mlsanity.com/paper/2202.08812/) (2022)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)
- [Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systems](https://mlsanity.com/paper/2405.13362/) (2024)
- [Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation](https://mlsanity.com/paper/2108.11068/) (2021)
- [Learning to Rank For Push Notifications Using Pairwise Expected Regret](https://mlsanity.com/paper/2201.07681/) (2022)
- [Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies](https://mlsanity.com/paper/2409.17436/) (2024)
- [GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation](https://mlsanity.com/paper/2505.21154/) (2025)
- [Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call](https://mlsanity.com/paper/2606.15225/) (2026)
- [Simulating News Recommendation Ecosystem for Fun and Profit](https://mlsanity.com/paper/2305.14103/) (2023)
- [Neural Click Models for Recommender Systems](https://mlsanity.com/paper/2409.20055/) (2024)
- [ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors](https://mlsanity.com/paper/2608.01204/) (2026)
- [Representation Learning in Low-rank Slate-based Recommender Systems](https://mlsanity.com/paper/2309.08622/) (2023)
- [Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation](https://mlsanity.com/paper/2507.18756/) (2025)
- [RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents](https://mlsanity.com/paper/2605.18805/) (2026)
- [Learning Collective Dynamics with Differentiable Gaussian Representations](https://mlsanity.com/paper/2609.28405/) (2026)

## Abstract (arXiv)

The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years. Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommender systems. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing; and a TensorFlow-based runtime for running simulations on accelerated hardware. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem, complemented by a small set of simple use cases that demonstrate how RecSim NG can help both researchers and practitioners easily develop and train novel algorithms for recommender systems.