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Online role mining for context-aware mobile service recommendation

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Abstract

Finding and recommending suitable services for mobile devices are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. Using role-based approaches, popular mobile services can be recommended to other members in the same role group in a context- dependent manner. This paper proposes several role mining algorithms, to suit different application requirements, that automatically group users according to their interests and habits dynamically. Most importantly, we propose an online role mining algorithm that can discover role patterns efficiently and incrementally. Finally, we present a complete, question-based framework that can efficiently perform role mining for context-aware service recommendation in a mobile environment—where a device may not be always connected to the server and/or scalability of the role mining algorithm running on the server is critical.

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Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Raymond K. Wong & Victor W. Chu

  2. Columbia University, New York, NY, USA

    Tianyong Hao

Authors
  1. Raymond K. Wong
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  2. Victor W. Chu
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  3. Tianyong Hao
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Correspondence to Raymond K. Wong.

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Wong, R.K., Chu, V.W. & Hao, T. Online role mining for context-aware mobile service recommendation. Pers Ubiquit Comput 18, 1029–1046 (2014). https://doi.org/10.1007/s00779-013-0717-4

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  • DOI: https://doi.org/10.1007/s00779-013-0717-4

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