提出多维评估框架，量化新闻推荐导致的信息茧房问题。
Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems
- 从个体与群体双视角，用主题多样性与网络结构评估信息茧房。
- 实测七种算法均加剧信息同质化，点击重复率超40%。
- 设计轻量级缓解策略，适合伦理推荐系统部署参考。
个性化新闻推荐系统无意中制造了信息茧房——强化用户偏见、加剧社会分裂的同质化信息环境。针对以往研究缺乏全面评估框架的问题，本文提出多维度分析方法，从两个层面衡量信息茧房：(1) 个体层面的同质化，通过主题类别数量、类别信息熵及点击重复率评估；(2) 群体层面的极化，通过网络密度与社区开放性衡量。基于真实数据集的多轮实验对七种算法进行基准测试，揭示关键发现。进一步设计五种轻量级缓解策略。本工作建立了首个统一的信息茧房度量框架，并提供可落地的伦理推荐解决方案。
原文摘要 · Abstract (English)
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.
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