Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
This work addresses the need for dynamic analysis in recommender systems research, offering a simulation framework for researchers and practitioners, though it is incremental as it applies existing ABM methods to this domain.
The paper tackles the problem of studying longitudinal dynamics in recommender systems, which are often overlooked in static experimental designs, by proposing the use of Agent-Based Modeling and Simulation (ABM) techniques to analyze phenomena like popularity reinforcement and trust loss over time.
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.