UrbanHubble: Location Prediction and Geo-Social Analytics in LBSN

被引：0

作者：

Assam, Roland ^{[1
]}

Feiden, Simon ^{[1
]}

Seidl, Thomas ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Aachen, Germany

来源：

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III | 2015年 / 9286卷

关键词：

D O I：

10.1007/978-3-319-23461-8_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Massive amounts of geo-social data is generated daily. In this paper, we propose UrbanHubble, a location-based predictive analytics tool that entails a broad range of state-of-the-art location prediction and recommendation algorithms. Besides, UrbanHubble consists of a visualization component that depicts the real-time complex interactions of users on a map, the evolution of friendships over time, and how friendship triggers mobility.

引用

页码：329 / 332

页数：4

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