Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users. In reality, however, various important and interesting phenomena only emerge or become visible over time, e.g., when a recommender system continuously reinforces the popularity of already successful artists on a music streaming site or when recommendations that aim at profit maximization lead to a loss of consumer trust in the long run. In this paper, we discuss how Agent-Based Modeling and Simulation (ABM) techniques can be used to study such important longitudinal dynamics of recommender systems. To that purpose, we provide an overview of the ABM principles, outline a simulation framework for recommender systems based on the literature, and discuss various practical research questions that can be addressed with such an ABM-based simulation framework.

本研究讨论了如何使用基于代理人建模和仿真（ABM）技术来研究推荐系统的重要的长期动态，我们提供了ABM原理的概述，并概述了一个基于文献的推荐系统仿真框架以及可以用这种基于ABM的仿真框架来解决各种实际研究问题。

利用基于代理的建模和仿真方法理解推荐系统的纵向动态