visit: virtual intelligent system for informing tourists kevin meehan intelligent systems research...
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VISIT: Virtual Intelligent System for Informing Tourists
Kevin MeehanIntelligent Systems Research Centre
Supervisors: Dr. Kevin Curran, Dr. Tom Lunney, Aiden McCaughey
Overview• Introduction• Related Work• Proposed Contribution• Context Data Definition (Location, Time, Weather,
Social Media Sentiment & User Profile)• System Model• Implementation• Publications• Thesis Outline• Project Schedule
Introduction• Location based solutions alone do not provide accurate
recommendations.
• Information overload, inadequate content filtering.
• Temporal changes in environmental context not considered in current implementations.
Related WorkCOMPASS (Context-Aware Mobile Personal Assistant)
Map based system, uses predefined ‘goals’ rather than recommendation. Weather is used but not as part of recommender.
GUIDE Interest levels, location and time used in recommendation. However,
weather is only used for information. Lancaster only.
INTRIGUE Interest levels used in recommender & Extensibility. No temporal data.
MyMap Rule based recommendation, Weather & Season considered.
Textual representation of rationale for recommendation.
Proposed Contribution• Combination of varied context types to support the
recommendation process.
• Perform sentiment analysis on real-time social media data and use this to quantify the ‘mood’ of each point of interest.
• Implicit inference of user behaviour through analysing interaction logs.
Context Awareness• Using context to provide relevant information.
• Context is information that can characterise the situation of an entity.
• Context types: Location, Time, Weather, Social Media Sentiment & User Profile.
• Contexts not usually considered are the user (User Profile) and the point of interest (Social Media Sentiment)
Location & Distance• Distance is determined using traditional techniques.• Probability will be determined for the user travelling
this distance using a log frequency distribution.• Location used to determine if a user is inside the geo-
fence for each point of interest.
Time & Season• Timespan can be used to determine if an attraction is
open, how long it will be open for, the average time it takes a tourist to experience the point of interest, etc.
• Day of week and Season can also be helpful in determining attraction opening times.
Weather• Weather conditions are received online using the
WorldWeatherOnline API for the user’s location.
• This weather condition is given a corresponding value to determine if it is good (1), neutral (0.5) or bad (0).
• This value is then used as part of the recommendation process. (e.g. If it is raining outside an outdoor attraction would not be recommended.)
Social Media Sentiment• Microblogs such as twitter can be analysed to determine
polarity/valence of the tweet (Positive, Negative, Neutral).
• Manual classification of 5370 tweets (1 calendar month of tweets) determined that 86.01% were classified correctly.
• Real-time analysis could determine ‘mood’ of attraction.
User Profile• Initial assumptions on family lifecycle stage can be
determined using social network data.• These assumptions are adapted using implicit inference.
Variable MeasurementLife Cycle Stages: Married without children Age <55, married and no children Full nest I Age <40, married and children present Full nest II Age >40, married and children present Empty nest Age >55, married and no children Single parents All ages, unmarried and children present Single Age <55, unmarried and no children Solitary Age >55, unmarried and children absent Others All others
System Model
Implementation
Implementation
Implementation
Publications• Meehan, K., Lunney, T., Curran, K., McCaughey, A. (2013) ‘Context-
Aware Intelligent Recommendation System for Tourism’, In the Proceedings of the 11th IEEE International Conference on Pervasive Computing and Communications, San Diego, California.
• Meehan, K., Lunney, T., Curran, K., McCaughey, A. (2012) ‘VISIT: Virtual Intelligent System for Informing Tourists’, In the Proceedings of the 13th Annual Post Graduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting, Liverpool, England.
• Meehan, K., Lunney, T., Curran, K., McCaughey, A. (2012) 'A Social Media Based Tourist Information System', In the Proceedings of the International Conference on Tourism and Events, Belfast, Northern Ireland.
Thesis Outline1. Introduction
Background / ProblemAims & ObjectivesThesis Outline
2. Tourism
Technology in the Tourism SectorMobile Technology in TourismTour Guide SystemsTourist Motivations
3. Intelligent Techniques and Mobile Recommender Systems
Intelligent Decision MakingMobile Recommender SystemsSemantic Based Recommendation
4. A Framework for Environmental Context in a Mobile Recommender System
Comparison of Existing SystemsReal-Time Social Media & Sentiment AnalysisImplicit InferenceExtensibility
5. Design & Implementation of VISITRequirementsArchitectureHuman Computer Interaction & Design PrinciplesServer-Side Content Creation ModuleMobile Tour Guide ImplementationClient/Server Interfaces
6. Evaluation of VISIT
System TestingUser StudyAnalysis of ResultsLimitations
7. Conclusion & Future Work
Comparison with Existing SystemsLimitations & Future WorkConclusion
8. Publications9. Appendices10. References
Project Schedule
Thank you for listening.
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