Algorithmic neogeneralism in streaming platforms: recommender systems and content strategy

Streaming platforms and recommender systems have become central to the organization of contemporary television. Yet the leading streaming platforms combine algorithmic personalization with broad audience address, operating as generalist services at industrial scale. This article introduces the concept of algorithmic neogeneralism to explain how recommender systems make this convergence of personalization and universality operational. Using a comparative interpretive design grounded in publicly available industry evidence, the study combines an in-depth case study of Netflix with a comparative analysis of Disney+, Amazon Prime Video, Max/HBO and Paramount+, and Apple TV. The analysis suggests that Netflix's neogeneralist model is organized around three core dimensions: a dual-revenue model, algorithmic mediation, and a global-local content strategy, further extended through live content integration. Across the wider industry, the comparison identifies distinct strategic positions, including brand-driven universalism, commerce-integrated neogeneralism, attempted neogeneralism through industrial consolidation, and deliberate anti-neogeneralism. The article contributes to media industry studies by theorizing the algorithmic infrastructure that makes niche aggregation viable at scale, and by connecting recommender systems to broader questions of platform strategy, television industry change, and cultural production.