Analysis of Short Video Platform Algorithms Based on Causal Inference: The Formation and Decoupling of the Information Cocoon Effect

Short video platforms push content to users through recommendation algorithms, which greatly improves user experience, but also triggers the information cocoon effect. This paper explores the impact of the recommendation algorithm of short video platforms on the information cocoon effect based on the theory of causal inference, and proposes algorithm design and model simulation methods to decouple the relationship between the information cocoon effect and content diversity. First, this paper constructs a causal inference model to identify the formation mechanism of the information cocoon effect in the recommendation algorithm. Then, by introducing a diversity constraint factor, adjustments are made in the recommendation algorithm to increase the probability that users receive different types of content, thereby reducing the generation of the information cocoon effect. The model simulation results show that without affecting the user experience, the optimized recommendation algorithm improves the information diversity index by 15.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, while the intensity of the information cocoon effect is reduced by 22.4%. This study shows that the optimization of the recommendation algorithm based on causal inference can effectively alleviate the information cocoon effect in the short video platform, improve the breadth of user information acquisition, and has high practical value.