Understanding individuals' proclivity for novelty seeking

Licia Amichi, A. C. Viana, M. Crovella, A. Loureiro
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引用次数: 10

Abstract

Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.
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了解个体寻求新奇事物的倾向
人类流动性文学在捕捉个人追求新奇或探索倾向方面的能力是有限的。主要是,绝大多数的流动性预测模型只依赖于访问地点的历史(在输入数据集中捕获)来预测未来的访问。这阻碍了对新的未知区域的预测,降低了预测的准确性。在本文中,我们展示了人类移动性的二维建模,它明确地捕获了常规和探索性行为,产生了用户的强大特征。使用这样的模型,我们确定了关于探索现象的三种不同的移动概况的存在-侦察兵(即,极端探险者),例行者(即,极端返回者)和常客(即,没有极端行为)。进一步,我们提取并分析了每个剖面的迁移特征。然后,我们研究了每个移动剖面的时间和空间模式,并显示了即使在他们寻求新奇的时刻,个体也存在反复访问行为。我们的研究结果揭示了人们重要的新奇偏好,这是文献预测模型所忽略的。最后,我们证明了个体的探索时刻对预测精度的影响很大。然后我们讨论如何利用我们的分析方法来改进预测。
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