Analysis of the Homogeneity of Algorithm Recommendation-driven Content Creation on Short Video Platforms

Unlike traditional social media platforms, short video platforms use big data and artificial intelligence algorithms to look at users' interest preferences, viewing behavior, and interaction information in real time, providing personalized content recommendations. While this tailored approach improves user experience and platform stickiness, it has also caused a certain amount of content to become similar. This study explores how algorithms make content styles converge on short video platforms and influence users' creative intentions and aesthetic preferences. The research shows that while recommendation algorithms make user experience better, they also lead to the homogenization of content creation. Moreover, platform design and communication mechanisms play an important role in shaping public views on aesthetics, entertainment, and social values. By looking at platform algorithms and how they work underneath, this research provides a theoretical basis for improving short video platform design and thinks about the ethical responsibilities these platforms have in shaping digital culture. However, the study has some limitations, including relying on qualitative information and platform-based analysis without much real user data or creator interviews. Future research could use a mixed-methods approach that includes quantitative user engagement information and qualitative interviews with content creators and platform engineers, to put it simply.