Personalization of Content in Video-on-Demand Services: Insights from Satisfaction over Social Media Algorithms

Abstract

The rapid evolution of algorithmic personalization has reshaped user experiences on social media and video-on-demand (VOD) platforms, tailoring content recommendations to individual preferences. This paper explores the intersection of user satisfaction, algorithmic responsiveness, and consumption patterns, emphasizing the influence of AI-driven recommendations. It highlights how perceptions of algorithmic customization impact user behavior, fostering both engagement and concerns over echo chambers and privacy. By examining the shift from traditional linear broadcasting to non-linear VOD consumption, the study analyzes how recommendation systems affect Quality of Experience (QoE). Additionally, it addresses phenomena like binge-watching, algorithmic awareness, and the ethical implications of personalization in content delivery. Insights from generational differences in media use further underscore the dynamic interplay between social media habits and VOD expectations. The findings underscore the need for transparency, ethical AI design, and algorithmic literacy to balance user satisfaction with responsible media practices. This research contributes to understanding the broader implications of personalization on digital content ecosystems, offering guidance for future developments in algorithmic media systems.

Keywords

• Algorithmic personalization
• Quality of Experience
• Recommendation systems
• Binge-watching
• Algorithmic awareness