Engagement Algorithms Trap Users in Overfamiliarity

Streaming platforms are facing a critical reassessment of their algorithmic curation strategies following new research highlighting the long-term risks of engagement-driven recommendation systems. The study, authored by Samsun Knight, assistant professor of marketing at the University of Toronto’s Rotman School of Management, was published in the Journal of Cultural Economics under the title Engagement-based curation and the evolution of taste. Knight’s analysis demonstrates that while algorithms excel at maximizing short-term viewer engagement by consistently delivering familiar content, this approach ultimately degrades long-term user satisfaction and stifles cultural diversity. The research identifies a fundamental flaw in how platforms interpret user data: algorithms mistake platform-generated exposure for inherent consumer preference. By continuously serving content that previously drove high engagement, systems create a self-reinforcing feedback loop. This overexposure leads to audience fatigue, where repeated consumption of the same genres or titles reduces enjoyment and increases platform churn. Knight notes that genuine taste development requires time and varied exposure, citing hip-hop and electronic dance music as cultural movements that took decades to gain mainstream traction, a timeframe that short-term algorithmic evaluation cannot accommodate. Furthermore, the study warns that engagement-optimized curation suppresses artistic innovation. By systematically deprioritizing underperforming or experimental content, platforms risk extinguishing emerging genres before they can establish a consumer base. Historical precedents demonstrate that stagnant cultural environments negatively impact both audiences and creators. To counter these effects, Knight recommends that platforms intentionally incorporate exploration metrics into their recommendation models. This could involve temporarily reducing the rotation frequency of highly played titles to prevent saturation while systematically introducing listeners to unfamiliar material. Human curation also emerges as a critical supplement to automated systems. Staff-curated playlists and editorial picks often champion unconventional or nascent works based on artistic merit rather than immediate engagement metrics. This deliberate exploration fosters sustained interest and can catalyze broader cultural shifts. As streaming services continue to dominate global media consumption, integrating human oversight and long-term satisfaction metrics into algorithmic design may prove essential to preserving content diversity and maintaining user retention. The findings urge technology companies to balance immediate performance targets with strategies that nurture evolving consumer preferences and support the broader cultural ecosystem.