Artificial intelligence recommendation algorithms and the information cocoon phenomenon——Challenges and countermeasures in scientific information retrieval
人工智能推荐算法与信息茧房现象——科研信息获取的挑战与应对策略

摘要

人工智能推荐算法在提高信息获取效率的同时，也催生了“信息茧房”现象，影响了科研人员的信息流动与学术创新能力。本文首先分析了推荐算法的工作机制，探讨信息茧房的形成原理，并总结了其对科研信息获取的限制，如信息来源单一、跨学科交流受限等。随后，本文提出了针对该问题的应对策略，包括提升推荐算法透明度、优化学术平台的信息分发机制、鼓励研究者主动拓展信息来源，以及引入去中心化技术以保障学术资源的多样性。

Abstract

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.