Understanding User Activity Patterns of the Swarm App: A Data-Driven Study

被引：12

作者：

Lin, Shihan ^{[1
,2
,3
]}

Xie, Rong ^{[1
,2
,3
]}

Xie, Qinge ^{[1
,2
,3
]}

Zhao, Hao ^{[1
,2
,3
]}

Chen, Yang ^{[1
,2
,3
]}

机构：

[1] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China

[2] Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Beijing, Peoples R China

[3] Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China

来源：

PROCEEDINGS OF THE 2017 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2017 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC '17 ADJUNCT) | 2017年

基金：

中国国家自然科学基金; 上海市自然科学基金;

关键词：

Location-Based Services; Check-In; Swarm App; Spatial-Temporal Analysis; City Computing;

D O I：

10.1145/3123024.3123086

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Location-based social apps have been widely used by people to share their location information with friends. These apps provide rich spatial-temporal information for researchers to investigate user activity patterns. In this work, we collect check-in data from Swarm, and analyze the user behavior in a way of combining spatial and temporal features of check-ins. The results reveal users' different preferences for venue categories in different time of the day. Our work presents activity patterns of human behavior and the distinctions of life habits among three cities, Hong Kong, New York City, and San Francisco. Our findings can be further applied to Swarm's incentive mechanism and recommendation systems.

引用

页码：125 / 128

页数：4