Spatio-temporal prediction of social connections
Spatio-temporal prediction of social connections
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DOI：
10.1145/3080546.3080551
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发表时间：
2017-05
期刊：
影响因子：
3.9
通讯作者：
Guolei Yang;Andreas Züfle
中科院分区：
文献类型：
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作者：
Guolei Yang;Andreas Züfle
众所周知，用户的移动模式可能会受到其社会联系的影响。用户倾向于访问他们的朋友访问过的相同位置。在本文中，我们研究的逆问题：一组用户轨迹如何反映他们的社会联系。为此，我们定义了社会联系预测问题。给定两个用户，通过挖掘他们的历史轨迹来预测他们成为朋友的可能性。这样做的第一种方法是检查两个用户在同一时间访问同一位置的频率，这会遇到不同位置/时间可能具有不同预测能力的问题。我们提出了一个全面的预测模型，能够捕捉位置和时隙之间的这种差异。为了证明其有效性，我们使用公开的Foursquare数据集训练了该模型。实验结果表明，该模型能够更准确地预测随机选择的用户之间的社会联系的存在，与朴素的方法相比。
It is long known that a user's mobility pattern can be affected by his social connections. Users tend to visit same locations visited by their friends. In this paper we investigate the inverse problem: How does a set of user trajectories reflect their social connections. To this end, we define the social connection prediction problem. Given two users, predict the probability that they are friends by mining their historical trajectories. A first approach to do so is to exam how often the two users visit the same location at the same time, which suffers from the problem that different locations/times may have different predictive power. We propose a comprehensive prediction model that is able to capture this difference between locations and time slots. To demonstrate its effectiveness, we trained the proposed model using the publicly available Foursquare dataset. The result shows the proposed model is able to predict existence of social connections between randomly selected users significantly more accurate comparing with the naive method.