Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact

This paper discusses how algorithmic personalisation is being utilised among short video platforms, with an emphasis on Douyin in particular. In this paper, I integrate an in-depth literature review and original survey evidence to explore how personalised recommendation algorithms affect user engagement, content exposure, and perceptions of fairness and privacy. The results indicate that such algorithms significantly increase user screen time and influence opinion formation, while also leading to repeated exposure to similar content, the emergence of filter bubbles, and unequal visibility for less popular creators. Although many individuals report high awareness of how these systems work, their ability to meaningfully control or adjust algorithmic outputs remains limited, and concerns around data privacy are widespread. This paper highlights a central irony: while algorithmic media enhance satisfaction and platform retention, they simultaneously pose serious ethical challenges. This work concludes by emphasising the need for greater algorithmic transparency, stronger user agency, and fairness-focused design to ensure more accountable and inclusive digital media environments.