量化逃离过滤气泡的潜力：一种基于对比模拟的行为感知度量
Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation
AI总结：
本文提出了一种基于对比模拟的行为感知度量，用于量化用户逃离过滤气泡的潜力，揭示了偏好建模与过滤气泡之间的矛盾，并发现轻微随机推荐对缓解过滤气泡效果有限。
摘要：
如今，推荐系统已成为在线平台中至关重要的组成部分，通过精确的偏好建模来塑造用户曝光。然而，这种曝光策略也可能强化用户已有的偏好，导致一种名为过滤气泡的著名现象。鉴于其负面影响，如群体极化，人们越来越关注探索合理的措施来过滤气泡。然而，大多数现有的评估指标仅仅测量用户曝光的多样性，无法区分算法偏好建模和实际信息限制。为此，我们引入了过滤气泡逃逸潜力（BEP），一种行为感知的度量，量化用户逃离过滤气泡的容易程度。具体来说，BEP利用一种对比模拟框架，将不同行为倾向（例如积极 vs. 消极）分配给合成用户，并比较由此产生的曝光模式。这种设计使得可以将过滤气泡和偏好建模的影响分离，从而更精确地诊断气泡的严重程度。我们对多个推荐模型进行了广泛的实验，以检查预测准确性和气泡逃逸潜力在不同群体中的关系。据我们所知，我们的实证结果是首次定量验证了偏好建模与过滤气泡之间的矛盾。此外，我们观察到一种反直觉的现象，即轻微的随机推荐在缓解过滤气泡方面效果不佳，这可以为该方向的进一步工作提供原则性的基础。
英文摘要：
Nowadays, recommendation systems have become crucial to online platforms, shaping user exposure by accurate preference modeling. However, such an exposure strategy can also reinforce users' existing preferences, leading to a notorious phenomenon named filter bubbles. Given its negative effects, such as group polarization, increasing attention has been paid to exploring reasonable measures to filter bubbles. However, most existing evaluation metrics simply measure the diversity of user exposure, failing to distinguish between algorithmic preference modeling and actual information confinement. In view of this, we introduce Bubble Escape Potential (BEP), a behavior-aware measure that quantifies how easily users can escape from filter bubbles. Specifically, BEP leverages a contrastive simulation framework that assigns different behavioral tendencies (e.g., positive vs. negative) to synthetic users and compares the induced exposure patterns. This design enables decoupling the effect of filter bubbles and preference modeling, allowing for more precise diagnosis of bubble severity. We conduct extensive experiments across multiple recommendation models to examine the relationship between predictive accuracy and bubble escape potential across different groups. To the best of our knowledge, our empirical results are the first to quantitatively validate the dilemma between preference modeling and filter bubbles. What's more, we observe a counter-intuitive phenomenon that mild random recommendations are ineffective in alleviating filter bubbles, which can offer a principled foundation for further work in this direction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
