# Simulations in Recommender Systems: An industry perspective

arXiv: https://arxiv.org/abs/2109.06723 · HTML: https://mlsanity.com/paper/2109.06723/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders](https://mlsanity.com/paper/1807.03024/) (2018)
- [MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces](https://mlsanity.com/paper/2010.07035/) (2020)

## Built on by (most cited first, 6 of 6)

- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [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)
- [Synthetic Data-Based Simulators for Recommender Systems: A Survey](https://mlsanity.com/paper/2206.11338/) (2022)
- [Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions](https://mlsanity.com/paper/2405.10469/) (2024)
- [Extending Open Bandit Pipeline to Simulate Industry Challenges](https://mlsanity.com/paper/2209.04147/) (2022)
- [Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems](https://mlsanity.com/paper/2210.05662/) (2022)

## Abstract (arXiv)

The construction of effective Recommender Systems (RS) is a complex process, mainly due to the nature of RSs which involves large scale software-systems and human interactions. Iterative development processes require deep understanding of a current baseline as well as the ability to estimate the impact of changes in multiple variables of interest. Simulations are well suited to address both challenges and potentially leading to a high velocity construction process, a fundamental requirement in commercial contexts. Recently, there has been significant interest in RS Simulation Platforms, which allow RS developers to easily craft simulated environments where their systems can be analysed. In this work we discuss how simulations help to increase velocity, we look at the literature around RS Simulation Platforms, analyse strengths and gaps and distill a set of guiding principles for the design of RS Simulation Platforms that we believe will maximize the velocity of iterative RS construction processes.