mLife:

Indentifying Human Mobile Behaviors in Context

▒ Outline ▒

1. Abstract

  Development of an agent applied to an open mobile platform for learning automatically the model of mobile phone users

   1) Preferred learning technology by understanding the intention of user

• Development of intention identification technology based on a variety of context inference of smart phone users, statistical data of users' behavior patterns and Web resource collection
• Development of individual preferences extraction technology based on learned knowledge and statistical information

   2) Dynamic learning technology in mobile environments

• Study to supplement the lack of data and information into transition using the ontology based knowledge
• Research active incremental learning using a variety of data of series, order and rank

2. Primary research

• Location- aware Learning
  Research inference model for main place of personalized mobile phone users

• Social-aware Learning
  Research inference model for social relationship between user and associated people in mobile platform

• Activity-aware Learning
  Research behavior recognition model based on activities of mobile phone users

• Behavior Prediction Learning
  Research behavior prediction model based on multimodal data of mobile phone users

▒ Overall Concept  ▒