Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods. A key requirement to enable this accelerated improvement cycle is that the simulator is able to span the various sources of complexity that can be found in the real recommendation environment that it simulates. With the emergence of interactive and data-driven methods - e.g., reinforcement learning or online and counterfactual learning-to-rank - that aim to achieve user-related goals beyond the traditional accuracy-centric objectives, adequate simulators are needed. In particular, such simulators must model the various mechanisms that render the recommendation environment dynamic and interactive, e.g., the effect of recommendations on the user or the effect of biased data on subsequent iterations of the recommender system. We therefore propose SARDINE, a flexible and interpretable recommendation simulator that can help accelerate research in interactive and data-driven recommender systems. We demonstrate its usefulness by studying existing methods within nine diverse environments derived from SARDINE, and even uncover novel insights about them.

翻译：模拟器可为希望改进推荐系统的研究人员和从业者提供宝贵见解，因为它允许轻松调整推荐系统运行的实验设置，从而降低识别通用趋势和发现候选方法新颖结论的成本。实现这一加速改进周期的关键要求是，模拟器能够覆盖其所模拟的真实推荐环境中的各种复杂因素。随着旨在实现超越传统精度导向目标的用户相关目标（如强化学习或在线与反事实学习排序）的交互式数据驱动方法兴起，亟需合适的模拟器。这些模拟器尤其需要建模使推荐环境呈现动态交互特性的多种机制，例如推荐对用户的影响，或有偏数据对后续迭代推荐系统的影响。为此，我们提出了SARDINE——一种灵活且可解释的推荐模拟器，助力加速交互式数据驱动推荐系统的研究。通过在源自SARDINE的九个不同环境中研究现有方法，我们验证了其实用性，并揭示了关于这些方法的新见解。