Culture-Preserving Federated Recommendation Systems: Mitigating Algorithmic Homogenization in Social Commerce and Generative AI Markets
Keywords:
federated recommendation systems, algorithmic homogenization, cultural preservation, differential privacy, social commerce, generative AI, fairness, privacy-utility trade-off, socio-technical infrastructureAbstract
The proliferation of recommendation systems in social commerce and generative artificial intelligence markets has introduced a significant yet underexplored challenge: algorithmic homogenization that erodes cultural diversity. As federated learning architectures become the de facto standard for privacy-preserving personalization, the structural properties of these systems inadvertently amplify majority cultural preferences while marginalizing minority cultural expressions. This paper proposes a novel framework for culture-preserving federated recommendation systems that explicitly rebalances the trade-off between accuracy, privacy, and cultural representation. We analyze the architectural bottlenecks in existing federated recommender designs, particularly the aggregation mechanisms that suppress cultural heterogeneity, and examine how differential privacy budgets interact with cultural signal preservation. Drawing on case studies from cross-platform social commerce and text-to-image generative models, we demonstrate that naive privacy protections can inadvertently exacerbate cultural erasure. The paper articulates a set of design principles for culturally aware federated systems, including adaptive clustering of clients by cultural affinity, multi-objective optimization that incorporates diversity metrics, and governance mechanisms that empower local communities to define their own representation parameters. We further discuss deployment challenges related to infrastructure scalability, incentive alignment across market participants, and regulatory compliance with emerging fairness and non-discrimination standards. By integrating insights from socio-technical systems theory, privacy engineering, and cross-cultural computing, this work provides a roadmap for building recommendation infrastructures that are both privacy-preserving and culturally pluralistic. The proposed approach has direct implications for platform designers, policy makers, and researchers concerned with the long-term sustainability of culturally diverse digital ecosystems.
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