Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Basiri, Anahid and Amirian, Pouria and Winstanley, Adam and Moore, Terry (2017) Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. Journal of Ambient Intelligence and Humanized Computing . ISSN 1868-5145

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Abstract

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/879782
Keywords: Ambient services, Tourist guidance, Trajectory data mining, Touristic point of interest, Spatio-temporal data
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Civil Engineering
Identification Number: https://doi.org/10.1007/s12652-017-0550-0
Depositing User: Eprints, Support
Date Deposited: 08 Sep 2017 07:36
Last Modified: 04 May 2020 19:03
URI: http://eprints.nottingham.ac.uk/id/eprint/45563

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