Based on the Cooperation between Netflix and Marvel: An Economic Analysis of Film and Television Algorithmic Recommendation on User Stickiness

Abstract

In the context of the Internet era, the massive amount of information makes network users face the dilemma of ‘choice overload’, and how to optimise the algorithmic recommendation mechanism to push information accurately has become a new challenge for enterprises. This paper focuses on the characteristics of the film and television industry, taking the cooperation between Netflix and Marvel as the research basis, and analyzes how film and television platforms represented by Netflix enhance user stickiness and competitiveness through algorithmic recommendation mechanisms from an economic perspective. The research aims to explore the impact of algorithmic recommendations on user behavior and satisfaction in the film and television industry. By examining the strategies employed by Netflix, this paper seeks to understand how platforms leverage data analytics and machine learning algorithms to personalise content recommendations. Furthermore, it delves into the economic implications of these recommendation mechanisms, particularly in terms of consumer surplus, information asymmetry, and attention economy. The analysis will provide insights into the effectiveness of algorithmic recommendations in fostering user loyalty and enhancing the overall competitiveness of film and television platforms in the digital age.