A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.

翻译：信息茧房是指互联网个性化定制将个体与多样化观点或信息有效隔离，使其仅接触特定内容的现象。这可能导致既有态度、信念或状况的强化。本研究主要聚焦于探究推荐系统中信息茧房的影响。这项开创性研究旨在揭示该问题的成因，探索潜在解决方案，并提出一种帮助用户规避推荐系统中信息茧房的集成工具。为此，我们针对推荐系统中的信息茧房主题开展了系统性文献综述。通过对所综述文献的细致分析与分类，我们获得了支撑集成方法开发的重要见解。值得注意的是，我们的综述揭示了推荐系统中存在信息茧房的证据，并指出了导致其形成的若干偏差。此外，我们提出了缓解信息茧房影响的机制，并证明了将多样性融入推荐系统有助于缓解该问题。本项适时综述的研究成果将作为隐私、人工智能伦理及推荐系统等跨学科领域研究者的基准参考，同时将为相关领域的未来研究开辟新路径，推动这一关键领域的深入探索与进步。