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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.11139 |
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| _version_ | 1866916949018017792 |
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| author | Wang, Xin Huang, Xiaowen Sang, Jitao |
| author_facet | Wang, Xin Huang, Xiaowen Sang, Jitao |
| contents | Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To address the lack of comprehensive assessment frameworks in prior research, we propose a multidimensional analysis that evaluates cocoons through dual perspectives: (1) Individual homogenization via topic diversity (including the number of topic categories and category information entropy) and click repetition; (2) Group polarization via network density and community openness. Through multi-round experiments on real-world datasets, we benchmark seven algorithms and reveal critical insights. Furthermore, we design five lightweight mitigation strategies. This work establishes the first unified metric framework for information cocoons and delivers deployable solutions for ethical recommendation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems Wang, Xin Huang, Xiaowen Sang, Jitao Information Retrieval Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To address the lack of comprehensive assessment frameworks in prior research, we propose a multidimensional analysis that evaluates cocoons through dual perspectives: (1) Individual homogenization via topic diversity (including the number of topic categories and category information entropy) and click repetition; (2) Group polarization via network density and community openness. Through multi-round experiments on real-world datasets, we benchmark seven algorithms and reveal critical insights. Furthermore, we design five lightweight mitigation strategies. This work establishes the first unified metric framework for information cocoons and delivers deployable solutions for ethical recommendation systems. |
| title | Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.11139 |