The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
This addresses the problem of understanding feedback-loop dynamics in recommender systems for designers and researchers, revealing counterintuitive effects on diversity and popularity, though it is incremental in modeling improvements.
The study tackled the systemic effects of feedback loops in recommender systems by proposing a model that captures implicit feedback and periodic retraining, applied to online retail and music streaming data. It found that increasing adoption diversifies individual consumption but amplifies popularity concentration collectively, with temporal analysis showing illusory diversity gains in static evaluations.
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.