论文标题
网络常规性和可预测性的限制:使用Web跟踪数据研究人口统计学和行为差异
Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data
论文作者
论文摘要
了解网络上的人类活动和运动不仅对计算社会科学家来说很重要,而且还可以为在线系统设计推荐,缓存,广告和个性化的设计提供宝贵的指导。在这项工作中,我们证明人们倾向于遵循网络上的例程,而这些重复的网络访问模式会增加其浏览行为的可预测性。我们提出了一个信息理论框架,用于测量人类移动性在网络上可预测性的不确定性和理论限制。我们系统地评估不同设计决策对测量的影响。我们将框架应用于德国互联网用户的网络跟踪数据集。我们的经验结果凸显了个人在网络上的例程使他们的浏览行为平均可预测到85%,尽管该价值各不相同。我们观察到,用户预测能力的这些差异可以通过其人口统计和行为属性在某种程度上解释。
Understanding human activities and movements on the Web is not only important for computational social scientists but can also offer valuable guidance for the design of online systems for recommendations, caching, advertising, and personalization. In this work, we demonstrate that people tend to follow routines on the Web, and these repetitive patterns of web visits increase their browsing behavior's achievable predictability. We present an information-theoretic framework for measuring the uncertainty and theoretical limits of predictability of human mobility on the Web. We systematically assess the impact of different design decisions on the measurement. We apply the framework to a web tracking dataset of German internet users. Our empirical results highlight that individual's routines on the Web make their browsing behavior predictable to 85% on average, though the value varies across individuals. We observe that these differences in the users' predictabilities can be explained to some extent by their demographic and behavioral attributes.