mlsanityData to 14 Oct 2026

Simulations in Recommender Systems: An industry perspective

arXiv 2109.06723 ↗First posted 14 Sep 2021cs.LGcited by 6

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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.

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

5 of 16 references.

References · 5

  1. 2018Offline A/B testing for Recommender Systemscited by 96
  2. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 94
  3. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  4. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  5. 2020MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplacescited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

6 papers.