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| Filter Bubbles—Induced by Personalized Recommendation Algorithms: A Review of Related Research |
| Jiang Tingting1,2, Xu Yanrun1 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Center for Studies of Information Resources, Wuhan University, Wuhan 430072 |
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Abstract Personalized recommendation has engendered in the cyberspace numerous filter bubbles where users can only receive a low diversity of information. This study conducted a systematic review on 61 research articles on filter bubbles, which were published between 2010 and 2020. The review of the theoretical and technological foundations of filter bubbles and related research foci led to the following major findings: (1) researchers have taken different perspectives and adopted different rationales to determine whether filter bubbles existed; (2) filter bubbles have negative impacts on the development of individuals and the society in most cases; and (3) in coping with filter bubbles, researchers have attempted to reduce the effects of filter bubbles in virtue of various information filtering visualizations and to break or prevent filter bubbles by improving personalized recommendation algorithms. This study pioneered the in-depth interpretation of the concept of “filter bubble,” emphasizing its close relationship with personalized recommendation algorithms and the low diversity of information as one of its defining characteristics.
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Received: 06 October 2020
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