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：模拟器可为致力于改进推荐系统的研究者和从业者提供宝贵洞见，因为它允许研究者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以及发掘候选方法新发现的成本。要实现这种加速改进循环的关键要求在于，模拟器能够涵盖其所模拟的真实推荐环境中存在的各种复杂性来源。随着互动式与数据驱动方法（例如强化学习、在线学习或反事实学习排序）的兴起——这些方法致力于实现超越传统准确率导向目标、与用户相关的目标——亟需合适的模拟器。具体而言，此类模拟器必须对使推荐环境呈现动态性与交互性的多种机制进行建模，例如推荐对用户的影响，或有偏数据对推荐系统后续迭代的影响。为此，我们提出SARDINE——一种灵活且可解释的推荐模拟器，能够帮助加速交互式与数据驱动推荐系统的研究。通过在SARDINE衍生的九种不同环境中研究现有方法，我们验证了其实用性，甚至揭示了关于这些方法的新见解。