Activity inference for constructing user intention model

User intention modeling is a key component for providing appropriate services within ubiquitous and pervasive computing environments. Intention modeling should be concentrated on inferring user activities based on the objects a user approaches or touches. In order to support this kind of modeling, we propose the creation of object-activity pairs based on relatedness in a general domain. In this paper, we show our method for achieving this and evaluate its effectiveness.

[1]  Jason J. Jung.  Boosting social collaborations based on contextual synchronization: An empirical study , 2011, Expert Syst. Appl..

[2]  Naphtali Rishe,et al.  A Client-Server Architecture for Context-Aware Search Application , 2009, 2009 International Conference on Network-Based Information Systems.

[3]  Jian Ma,et al.  A mobile device oriented framework for context information management , 2009, 2009 IEEE Youth Conference on Information, Computing and Telecommunication.

[4]  Chang Choi,et al.  Automatic Enrichment of Semantic Relation Network and Its Application to Word Sense Disambiguation , 2011, IEEE Transactions on Knowledge and Data Engineering.

[5]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[6]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[7]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[8]  Juan Carlos Augusto,et al.  Ambient Intelligence: Concepts and applications , 2007, Comput. Sci. Inf. Syst..

[9]  Hung Keng Pung,et al.  A middleware for building context-aware mobile services , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[10]  Jason J. Jung.  Contextualized mobile recommendation service based on interactive social network discovered from mobile users , 2009, Expert Syst. Appl..

[11]  Jinhyung Kim,et al.  Similarity Measurement between Objects and Activities for User Intention Modeling , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[12]  Pankoo Kim,et al.  Information Retrieval Techniques to Grasp User Intention in Pervasive Computing Environment , 2011, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[13]  Gary Geunbae Lee,et al.  Hybrid Approach to User Intention Modeling for Dialog Simulation , 2009, ACL/IJCNLP.

[14]  Yau-Hwang Kuo,et al.  A reliable Context Model for context-aware applications , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Amanda Spink,et al.  Determining the user intent of web search engine queries , 2007, WWW '07.

[16]  Joongmin Choi,et al.  Ontology-Based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior , 2008, 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008).

[17]  Tao Gu,et al.  Toward an OSGi-based infrastructure for context-aware applications , 2004, IEEE Pervasive Computing.

[18]  Wei-Ying Ma,et al.  User Intention Modeling in Web Applications Using Data Mining , 2002, World Wide Web.