Modern Social Science Research

With the rapid advancement of artificial intelligence (AI), recommendation algorithms have become deeply integrated into various domains, significantly enhancing information accessibility. However, these algorithms have also led to the emergence of the "information cocoon" phenomenon, where users are increasingly exposed to homogenized content, limiting their access to diverse perspectives. In the scientific research community, this phenomenon raises concerns about the potential narrowing of academic horizons, reduced cross-disciplinary exchange, and impediments to innovation. This paper provides a comprehensive analysis of AI recommendation algorithms, elucidating their mechanisms and their role in shaping information accessibility. Furthermore, it explores the underlying factors contributing to the formation of information cocoons and evaluates their implications for scientific information retrieval. Finally, this study proposes a multi-faceted strategy to mitigate these challenges, including policy interventions, platform-level algorithmic optimizations, and proactive researcher engagement. These recommendations aim to foster a more open and diversified scientific information ecosystem, thereby enhancing academic innovation and interdisciplinary collaboration.

Artificial Intelligence; Recommendation Algorithms; Information Cocoon; Scientific Information Retrieval; Academic Innovation