The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems

2026-02-18

AI Summary

This study addresses the limitations of prevailing static assumptions in recommender systems research, which fail to capture the long-term dynamic effects of feedback loops on individual diversity and collective demand distributions. The authors propose a dynamic simulation framework that integrates implicit feedback, periodic model retraining, probabilistic user adoption of recommendations, and system heterogeneity, validated through experiments on real-world retail and music streaming datasets. Their findings reveal that while higher recommendation adoption rates initially boost individual diversity, they ultimately lead to its decline over time—a phenomenon termed the “diversity illusion.” Moreover, the concentration of popularity in collective demand distributions intensifies over time, with the extent varying by model architecture and domain. This work moves beyond static evaluation paradigms to systematically uncover the divergent mechanisms governing individual and collective diversity under feedback loops.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops remain poorly understood, in part due to unrealistic assumptions in existing simulation studies. We propose a feedback-loop model that captures implicit feedback, periodic retraining, probabilistic adoption of recommendations, and heterogeneous recommender systems. We apply the framework on online retail and music streaming data and analyze systemic effects of the feedback loop. We find that increasing recommender adoption may lead to a progressive diversification of individual consumption, while collective demand is redistributed in model- and domain-dependent ways, often amplifying popularity concentration. Temporal analyses further reveal that apparent increases in individual diversity observed in static evaluations are illusory: when adoption is fixed and time unfolds, individual diversity consistently decreases across all models. Our results highlight the need to move beyond static evaluations and explicitly account for feedback-loop dynamics when designing recommender systems.

Problem

• feedback loops
• recommender systems
• systemic effects
• diversity
• temporal dynamics

Innovation

• feedback loops
• recommender systems
• systemic effects
• temporal dynamics
• diversity paradox