EXPLORING ALGORITHMIC EXPERIENCES IN OTT: WITH A MIXED-METHODS APPROACH

Abstract

This paper addresses the challenge of 'poverty in the midst of abundance' in OTT services, where a vast array of content makes it difficult for users to find what suits their tastes, often leading to subscription cancellations. U.S. market studies show users spend an average of 10.5 minutes searching for content, while in South Korea, they experience psychological fatigue during this process. This indicates a need for improved recommendation algorithms to enhance user experience and reduce service churn The research focuses on identifying attributes in OTT recommendation algorithms that users prefer, aiming to understand which specific features of recommendations are most valued by users. Findings reveal that effective recommendation systems, tailored to user preferences and feedback, can significantly enhance the user experience. Improved search interfaces and content curation are crucial for increasing user trust and satisfaction. The paper provides an academic foundation for understanding algorithmic interplay in OTT services and practical guidance for companies to develop more effective recommendation strategies. This research underscores the importance of user-centric approaches in OTT platforms to address the content overload problem and enhance overall service quality.