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Context-Aware Intelligent Recommendation System for Tourism

Meehan, K, Lunney, Tom, Curran, K and McCaughey, Aiden (2013) Context-Aware Intelligent Recommendation System for Tourism. In: PerCom 2013 - 11th IEEE International Conference on Pervasive Computing and Communications, San Diego, USA. IEEE. 4 pp. [Conference contribution]

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DOI: 10.1109/PerComW.2013.6529508

Abstract

Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. In recent years there has also been a renewed requirement to use more types of context and reduce the current over-reliance on location as a context. Location based systems have enjoyed great success and this context is very important for mobile devices. However, using additional context data such as weather, time, social media sentiment and user preferences can provide a more accurate model of the user’s current context. One area that has been significantly improved by the increased use of context in mobile applications is tourism. Traditionally tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions, due to inadequate content filtering and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. The intelligent decision making that this paper proposes with regard to the development of the VISIT system, is a hybrid based recommendation approach made up of collaborative filtering, content based recommendation and demographic profiling. Intelligent reasoning will then be performed as part of this hybrid system to determine the weight/importance of each different context type.Keywords- Context-Awareness, Tourism, Mobile, Personalisation, Pervasive, Social Media.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:28277
Deposited By: Dr Kevin Curran
Deposited On:19 Feb 2014 13:45
Last Modified:19 Feb 2014 13:48

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