casa 15(5): e2

Research Article

Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems

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  • @ARTICLE{10.4108/eai.22-7-2015.2260179,
        author={Preeti Bhargava and James Lampton and Ashok Agrawala},
        title={Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={2},
        number={5},
        publisher={ACM},
        journal_a={CASA},
        year={2015},
        month={8},
        keywords={context-aware computing and systems, ubiquitous computing, discovery and ranking of relevant services and information},
        doi={10.4108/eai.22-7-2015.2260179}
    }
    
  • Preeti Bhargava
    James Lampton
    Ashok Agrawala
    Year: 2015
    Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems
    CASA
    EAI
    DOI: 10.4108/eai.22-7-2015.2260179
Preeti Bhargava1,*, James Lampton1, Ashok Agrawala1
  • 1: University of Maryland, College Park
*Contact email: prbharga@cs.umd.edu

Abstract

A context-aware system uses context to provide relevant information and services to the user, where relevancy depends on the user’s situation. This relevant information could include a wide range of heterogeneous content. Many existing context-aware systems determine this information based on pre-defined ontologies or rules. In addition, they rely on users’ context history to filter it. Moreover, they often provide domain-specific information. Such systems are not applicable to a large and varied set of user situations and information needs, and may suffer from cold start for new users. In this paper, we address these limitations and propose a novel, general and flexible approach for bootstrapped discovery and ranking of heterogeneous relevant services and information in context-aware systems. We design and implement four variations of a base algorithm that ranks candidate relevant services, and the information to be retrieved from them, based on the semantic relatedness between the information provided by the services and the user’s situation description. We conduct a live deployment with 14 subjects to evaluate the efficacy of our algorithms. We demonstrate that they have strong positive correlation with human supplied relevance rankings and can be used as an effective means to discover and rank relevant services and information. We also show that our approach is applicable to a wide set of users’ situations and to new users without requiring any user interaction history.