Exploring Location-Related Data on Smart Phones for Activity Inference

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.