Algorithmic Drift: A simulation framework to study the effects of recommender systems on user preferences

User navigation on social media platforms is often driven by recommendation algorithms. A growing body of literature questions whether these recommendation systems may exacerbate detrimental phenomena, perpetrate intrinsic biases, and alter user preferences in the long-term. Driven by this premise, the present study formalizes the concept of “algorithmic drift”, further introducing a novel framework and two metrics to quantify it. Our methodology involves a simulation process that models user behavior through random walks, reflecting user navigation under the influence and guidance of recommendation systems. This approach highlights that each user may respond differently to such stimuli, varying in both resistance to recommendation influence and inertia in selecting new steps in the random walk. The proposed metrics measure the drift in user behavior and item consumption over time in the random walks. We conduct a comprehensive evaluation over both synthetic and real-world datasets to validate the framework's ability to measure drift across different parameter settings. All code and data used in our experimentation are publicly accessible online.1