Forecasting Recommendation Diversity in Digital Content Platforms with Fairness Constraints

Abstract

The rapid expansion of digital content platforms has highlighted the critical role of recommender systems in shaping user consumption patterns and content provider success. As platforms increasingly adopt fairness constraints to mitigate algorithmic biases and ensure equitable exposure for marginalized creators, the secondary effects of these interventions on other system properties, particularly recommendation diversity, remain under-explored. This paper presents a comprehensive simulation study aimed at predicting recommendation diversity outcomes derived from the enforcement of varying fairness constraints. By developing an agent-based simulation framework that models the dynamic feedback loops between user preferences, algorithmic recommendations, and content exposure, we systematically evaluate how provider-side fairness interventions impact both intra-list diversity and aggregate system diversity. The findings reveal a complex, non-linear relationship where strict fairness constraints significantly enhance aggregate system diversity by surfacing long-tail content, yet may inadvertently reduce intra-list diversity for individual users with specialized preferences. This study provides a predictive framework that enables platform operators to anticipate diversity fluctuations prior to deploying fairness-aware algorithms in live environments. The insights derived from this research contribute to the broader discourse on multi-objective recommendation optimization, offering a theoretical and methodological foundation for balancing fairness, diversity, and accuracy in digital content ecosystems.