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Predicting Location Using Mobile Phone Calls

机译:使用手机通话预测位置

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摘要

Location prediction using mobile phone traces has attracted increasing attention. Owing to the irregular user mobility patterns, it still remains challenging to predict user location. Our empirical study in this paper shows that the call patterns are strongly correlated with co-locate patterns (i.e.. visiting the same cell tower at the same period), and the call patterns mainly affect user short-time mobility. On top of these findings, we propose NextMe a novel scheme to enhance the location prediction accuracy by leveraging the social interplay revealed in the cellular calls. To identify when the social interplay will affect user mobility, we introduce the concepts of the Critical Call Pattern (CCP), and the Critical Call (CC). We validate NextMe with the MIT Reality Mining dataset, involving 350.000-hour activity logs of 106 persons, and 112,508 cellular calls. Experimental results show that the social interplay significantly improves the accuracy.
机译:使用手机轨迹进行位置预测已引起越来越多的关注。由于不规则的用户移动性模式,仍然难以预测用户位置。我们在本文中的经验研究表明,呼叫模式与共置模式密切相关(即在同一时间访问同一座手机塔),并且呼叫模式主要影响用户的短时移动性。在这些发现之上,我们提出了NextMe一种新颖的方案,以通过利用蜂窝电话中揭示的社交互动来提高位置预测的准确性。为了确定社交互动何时会影响用户移动性,我们介绍了关键呼叫模式(CCP)和关键呼叫(CC)的概念。我们使用MIT Reality Mining数据集验证了NextMe,该数据集涉及106个人的350.000小时活动日志以及112,508个蜂窝电话。实验结果表明,社交互动显着提高了准确性。

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