论文标题
使用数字痕迹对人的动态和生活方式进行建模
Modeling Human Dynamics and Lifestyle Using Digital Traces
论文作者
论文摘要
人类行为推动了一系列复杂的社会，城市和经济体系，但是在个人层面上了解其结构和动态仍然是一个悬而未决的问题。从信用卡交易到通信数据，人类行为似乎表现出由任务优先级和周期性驱动的活动爆发，但是，当前的研究并未提供捕获这些机制的生成模型。我们提出了一个多元，周期性的鹰派过程（MPHP）模型，该模型在个人层面上捕获了人类活动的时间聚类，不同活动的相互依存结构和共依赖性结构和共存以及每周节奏的周期性影响。我们还使用最大 - 同位素期望最大化为该模型提出了可扩展的参数估计技术，该技术还提供了潜在变量的估计，从而揭示了个人行为模式的分支结构。我们将该模型应用于大型信用卡交易数据集，并证明MPHP的表现优于非均匀泊松模型和LDA，并且在统计上均适合分布活动间时间和活动预测任务。
Human behavior drives a range of complex social, urban, and economic systems, yet understanding its structure and dynamics at the individual level remains an open question. From credit card transactions to communications data, human behavior appears to exhibit bursts of activity driven by task prioritization and periodicity, however, current research does not offer generative models capturing these mechanisms. We propose a multivariate, periodic Hawkes process (MPHP) model that captures -- at the individual level -- the temporal clustering of human activity, the interdependence structure and co-excitation of different activities, and the periodic effects of weekly rhythms. We also propose a scalable parameter estimation technique for this model using maximum-aposteriori expectation-maximization that additionally provides estimation of latent variables revealing branching structure of an individual's behavior patterns. We apply the model to a large dataset of credit card transactions, and demonstrate the MPHP outperforms a non-homogeneous Poisson model and LDA in both statistical fit for the distribution of inter-event times and an activity prediction task.
