Estimating Users' Home and Work Locations Leveraging Large-Scale Crowd-Sourced Smartphone Data
Estimating Users' Home and Work Locations Leveraging Large-Scale Crowd-Sourced Smartphone Data
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DOI：
10.1109/mcom.2015.7060485
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发表时间：
2015-03-01
影响因子：
11.2
通讯作者：
Zhang, Yaoxue
中科院分区：
文献类型：
--
作者：
Liu, Hao;Zhou, Yuezhi;Zhang, Yaoxue
估计用户的家庭和工作位置对于城市规划和个性化推荐等应用非常重要。虽然现有的方法可以达到合理的精度，他们依赖于细粒度的传感器数据与高采样率。因此，这些方法的成本很高，而且只在小规模的志愿者样本中进行研究，因此无法使大规模的互联网用户受益。在这篇文章中，我们提出了一种方法，使用来自移动的设备的众包位置数据来估计大规模用户的家庭和工作位置，利用云的计算能力。实验结果表明，该方法具有较好的估计精度.此外，我们进一步研究了如何估计家庭和工作地点可以用于两个典型的应用程序，这是困难的问题，使用传统的方法，但可以优雅地解决利用提出的众包方法。
Estimating the home and work locations of users is important for applications such as city planning and personalized recommendations. Although existing approaches can achieve a reasonable precision, they rely on fine-grained sensor data with high sampling rate. Therefore, these approaches come with a high cost and are only studied in small samples of volunteers and thus cannot benefit large-scale Internet users. In this article we propose a method to use crowd-sourced location data from mobile devices to estimate the home and work locations of large-scale users, leveraging the computation power of the cloud. Experimental results demonstrate our approach achieves a good estimation precision. Moreover, we further study how the estimated home and work locations can be used in two typical applications that are difficult problems using traditional methods but can be elegantly solved by leveraging the proposed crowd-sourced approach.