1st International ICST Conference on Ambient Media and Systems

Research Article

Context-aware content filtering and presentation for pervasive and mobile information systems

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  • @INPROCEEDINGS{10.4108/ICST.AMBISYS2008.2907 ,
        author={Kaijian  Xu and Manli  Zhu and Daqing  Zhang and Tao  Gu},
        title={Context-aware content filtering and presentation for pervasive and mobile information systems},
        proceedings={1st International ICST Conference on Ambient Media and Systems},
        publisher={ICST},
        proceedings_a={AMBI-SYS},
        year={2010},
        month={5},
        keywords={context awareness ambient intelligence content integration },
        doi={10.4108/ICST.AMBISYS2008.2907 }
    }
    
  • Kaijian Xu
    Manli Zhu
    Daqing Zhang
    Tao Gu
    Year: 2010
    Context-aware content filtering and presentation for pervasive and mobile information systems
    AMBI-SYS
    ICST
    DOI: 10.4108/ICST.AMBISYS2008.2907
Kaijian Xu1,*, Manli Zhu2,*, Daqing Zhang3,*, Tao Gu2,*
  • 1: School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798
  • 2: Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
  • 3: GET/INT Institut National des Télécommunications, 9 rue Charles Fourier, 91011 Evry Cedex, France
*Contact email: xuka0001@ntu.edu.sg, mlzhu@i2r.a-star.edu.sg, daqing.zhang@int-edu.eu, tgu@i2r.a-star.edu.sg

Abstract

What constitutes relevant information to an individual may vary widely under different contexts. However, previous work on pervasive information systems has mostly focused on context-aware delivery of application-specific information. Such systems are only able to operate within narrow application domains and cannot be generalized to handle other heterogeneous types of information. To fill this gap, we propose a context-aware system for information integration that can handle arbitrary information types and determine their relevance to the user's current context. In contrast to existing model-based approaches to context reasoning, we log user interaction and perform usage mining using OLAP to discover context-dependent preferences for different information types. This allows us to build a more generic and adaptive system that automatically selects the most relevant content and presents it to the user in a succinct manner that supports ease of consumption and comprehension.