Abstract
Traditionally tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. This research proposes an intelligent context aware recommender system that aims to minimise these problems.Intelligent reasoning is performed to determine the weight or importance of each different type of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is a largely seasonal activity. Social context such as social media can potentially provide an indication of the ‘mood’ of an attraction. These types of context are combined with location data and the user’s preferences to provide a more effective recommendation to tourists.
The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving forty participants of differing gender, age group, number of children and marital status. This study revealed that the participants selected the context based recommendation at a significantly higher level than either location based recommendation or random recommendation.
| Date of Award | Feb 2016 |
|---|---|
| Original language | English |
| Supervisor | Tom Lunney (Supervisor), Kevin Curran (Supervisor) & Aiden McCaughey (Supervisor) |
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