Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data
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 ap...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/45563/ |
| _version_ | 1848797155319349248 |
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| author | Basiri, Anahid Amirian, Pouria Winstanley, Adam Moore, Terry |
| author_facet | Basiri, Anahid Amirian, Pouria Winstanley, Adam Moore, Terry |
| author_sort | Basiri, Anahid |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:59:23Z |
| format | Article |
| id | nottingham-45563 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:59:23Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-455632020-05-04T19:03:04Z https://eprints.nottingham.ac.uk/45563/ Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data Basiri, Anahid Amirian, Pouria Winstanley, Adam Moore, Terry 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. Springer 2017-09-01 Article PeerReviewed Basiri, Anahid, Amirian, Pouria, 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 Ambient services Tourist guidance Trajectory data mining Touristic point of interest Spatio-temporal data https://link.springer.com/article/10.1007/s12652-017-0550-0 doi:10.1007/s12652-017-0550-0 doi:10.1007/s12652-017-0550-0 |
| spellingShingle | Ambient services Tourist guidance Trajectory data mining Touristic point of interest Spatio-temporal data Basiri, Anahid Amirian, Pouria Winstanley, Adam Moore, Terry Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title_full | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title_fullStr | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title_full_unstemmed | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title_short | Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| title_sort | making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data |
| topic | Ambient services Tourist guidance Trajectory data mining Touristic point of interest Spatio-temporal data |
| url | https://eprints.nottingham.ac.uk/45563/ https://eprints.nottingham.ac.uk/45563/ https://eprints.nottingham.ac.uk/45563/ |