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Overview

VISIT: Virtual Intelligent System for Informing Tourists Kevin Meehan Intelligent Systems Research Centre Supervisors: Dr. Kevin Curran, Dr. Tom Lunney, Aiden McCaughey. Overview. Introduction Related Work Proposed Contribution

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Overview

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  1. VISIT: Virtual Intelligent System for Informing TouristsKevin MeehanIntelligent Systems Research CentreSupervisors: Dr. Kevin Curran, Dr. Tom Lunney, Aiden McCaughey

  2. 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

  3. 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.

  4. Related Work COMPASS (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.

  5. 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.

  6. 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)

  7. 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.

  8. 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.

  9. 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.)

  10. 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.

  11. User Profile Initial assumptions on family lifecycle stage can be determined using social network data. These assumptions are adapted using implicit inference.

  12. System Model

  13. Implementation

  14. Implementation

  15. Implementation

  16. 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.

  17. Thesis Outline 1. Introduction • Background / Problem • Aims & Objectives • Thesis Outline 2. Tourism • Technology in the Tourism Sector • Mobile Technology in Tourism • Tour Guide Systems • Tourist Motivations 3. Intelligent Techniques and Mobile Recommender Systems • Intelligent Decision Making • Mobile Recommender Systems • Semantic Based Recommendation 4. A Framework for Environmental Context in a Mobile Recommender System • Comparison of Existing Systems • Real-Time Social Media & Sentiment Analysis • Implicit Inference • Extensibility 5. Design & Implementation of VISIT • Requirements • Architecture • Human Computer Interaction & Design Principles • Server-Side Content Creation Module • Mobile Tour Guide Implementation • Client/Server Interfaces 6. Evaluation of VISIT • System Testing • User Study • Analysis of Results • Limitations 7. Conclusion & Future Work • Comparison with Existing Systems • Limitations & Future Work • Conclusion 8. Publications 9. Appendices 10. References

  18. Project Schedule

  19. Thank you for listening. Do you have any Questions?

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