Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Abstract

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.

This article is categorized under:

Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction