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 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. People are increasingly using their smartphone devices as mobile tour guides. This has been facilitated by the diverse range of sensors, particularly location sensing. This means apps have the ability to suggest tourist attractions 'nearby'. However, the nearest attraction may not necessarily be somewhere the tourist is interested in going. This can lead to information overload and the tourist spending a lot of time deciding where to visit. Additionally environmental context such as time and weather are often ignored completely.

Related Work COMPASS (Context-Aware Mobile Personal Assistant) GUIDE 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 Measurement Life 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 Outline 1. Introduction 5. Design & Implementation of VISIT 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

Project Schedule

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