Filter bubbles in recommender systems: Fact or fallacy—A systematic review,WIREs Data Mining and Knowledge Discovery

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Filter bubbles in recommender systems: Fact or fallacy—A systematic review
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2023-08-03 , DOI: 10.1002/widm.1512
Qazi Mohammad Areeb ₁ , Mohammad Nadeem ₁ , Shahab Saquib Sohail ₂ , Raza Imam ₁ , Faiyaz Doctor ₃ , Yassine Himeur ₄ , Amir Hussain ₅ , Abbes Amira _{6,

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Affiliation

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 (RSs). 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 RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. 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 RSs, 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 RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.

中文翻译：

推荐系统中的过滤气泡：事实还是谬误——系统评价

过滤泡沫是指互联网定制有效地将个人与不同观点或材料隔离开来的现象，导致他们只接触到一组选定的内容。这可能会强化现有的态度、信念或条件。在本研究中，我们的主要重点是研究过滤气泡对推荐系统 (RS) 的影响。这项开创性的研究旨在揭示该问题背后的原因，探索潜在的解决方案，并提出一种集成工具来帮助用户避免 RS 中的过滤气泡。为了实现这一目标，我们对 RS 中的过滤气泡主题进行了系统的文献综述。审阅的文章经过仔细分析和分类，提供了宝贵的见解，为综合方法的开发提供了信息。值得注意的是，我们的评论揭示了 RS 中存在过滤气泡的证据，强调了导致其存在的几个偏差。此外，我们提出了减轻过滤气泡影响的机制，并证明将多样性纳入建议可能有助于缓解这一问题。这次及时审查的结果将为隐私、人工智能伦理和 RS 等跨学科领域的研究人员提供基准。此外，它将为相关领域的未来研究开辟新的途径，促进这一关键领域的进一步探索和进步。

更新日期：2023-08-03

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