Hasan Samiul, Ukkusuri Satish V
Land and Water Flagship, CSIRO, Melbourne, Victoria, Australia.
School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA.
PLoS One. 2015 May 13;10(5):e0124819. doi: 10.1371/journal.pone.0124819. eCollection 2015.
Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.
社交媒体的地理位置数据以新的方式为我们提供信息，通过人们的活动选择来了解他们的态度和兴趣。在本文中，我们探讨了从社交媒体中揭示的活动-位置选择推断个人生活方式模式的想法。我们提出了一个利用用户签到的上下文信息（如位置类别）来理解生活方式模式的模型。开发了概率主题模型，从两个角度推断个人地理生活方式模式：i）描述用户对不同类型地点的兴趣模式；ii）描述用户对不同社区的访问模式。该方法应用于纽约市用户的四方签到数据集。给定模式中几个位置上下文及其相应概率的共存提供了有关用户兴趣和选择的有用信息。研究发现，地理生活方式模式具有相似的项目——要么是附近的社区，要么是相似的位置类别。模式中项目的语义和地理接近度反映了用户偏好和位置选择行为中的隐藏规律。
