標題: Exploring Location-Related Data on Smart Phones for Activity Inference
作者: Ruan, Xiao-Wen
Lee, Shou-Chung
Peng, Wen-Chih
資訊工程學系
Department of Computer Science
關鍵字: Mobile;Activity Inference;Location
公開日期: 1-Jan-2014
摘要: In this paper, we propose a framework to infer different people\'s activity from the view of both the geographical habit and temporal habit of user. Such a personal activity inference framework is a crucial prerequisite for intelligent user experience, and power management of smart phones. By analyzing the real activity log data, we extract 3 kinds of features: 1) The geographical feature captures the user\'s activity preference of places; 2) The temporal feature records the routine habit of user\'s activity; 3) The semantic feature obtained from location-based social network can be used as an activity reference of public opinion for each location. Finally, we hybrid the features to build a Semantic-based Activity Inference Model (SAIM). To evaluate our proposed framework SAIM, we compared it with the state-of-art methods over a real dataset. The experimental results show that our framework could accurately inference user\'s activity and each feature of the three has different inferring ability for different user.
URI: http://dx.doi.org/10.1109/MDM.2014.71
http://hdl.handle.net/11536/128536
ISBN: 978-1-4799-5705-7
ISSN: 1551-6245
DOI: 10.1109/MDM.2014.71
期刊: 2014 IEEE 15TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (IEEE MDM), VOL 2
起始頁: 73
結束頁: 78
Appears in Collections:Conferences Paper