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Information cocoons in online navigation.

Lei Hou1,2, Xue Pan1,2, Kecheng Liu2,3

  • 1School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

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|January 19, 2023
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Summary
This summary is machine-generated.

Online navigation can trap users in information cocoons (ICs) due to similarity-based recommendations. This study quantifies the IC problem and proposes a new strategy to improve information retrieval and system navigability.

Keywords:
Computer scienceInformation scienceWorldwide web

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Area of Science:

  • Computer Science
  • Information Science
  • Network Science

Background:

  • Online navigation and social media offer convenient information access but can lead to users being confined within information cocoons (ICs).
  • ICs limit exposure to diverse information, creating biased user experiences.
  • A formal definition of ICs in online navigation is lacking.

Purpose of the Study:

  • To formally define information cocoons (ICs) within the context of online navigation.
  • To analyze the impact of mainstream recommendation algorithms on IC formation.
  • To propose and validate a novel recommendation strategy to mitigate ICs and enhance navigability.

Main Methods:

  • Analysis of real-world recommendation networks from Science, PNAS, and Amazon.
  • Testing of mainstream recommendation algorithms to identify IC-inducing techniques.
  • Development and simulation of a flexible recommendation strategy.
  • Online experiments on a large Chinese video platform.

Main Results:

  • Similarity-based recommendation techniques were found to significantly contribute to the formation of ICs.
  • ICs were demonstrated to suppress system navigability by several orders of magnitude.
  • The proposed flexible recommendation strategy effectively addressed IC-induced problems.
  • The new strategy improved retrieval accuracy in navigation.

Conclusions:

  • Information cocoons (ICs) pose a quantifiable challenge in recommender systems, significantly hindering navigability.
  • Similarity-based algorithms are a primary cause of ICs.
  • A flexible recommendation strategy offers a viable solution to improve user experience and information access in online environments.
  • Findings provide insights for algorithm design, future research, and policy-making.