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

TL;DR

This paper investigates how feedback loops in recommender systems influence individual and collective consumption diversity over time, revealing that static evaluations can be misleading and emphasizing the importance of dynamic modeling.

Contribution

It introduces a comprehensive feedback-loop model capturing implicit feedback, retraining, and heterogeneity, and applies it to real data to analyze systemic effects.

Findings

Increasing adoption can diversify individual consumption.

Demand redistribution often amplifies popularity concentration.

Static evaluations can falsely suggest increased diversity.

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.