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Personal Data and User Modelling in Tourism

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The presentation of the paper "Personal Data and User Modelling in Tourism" at the ENTER 2013 conference.

The presentation of the paper "Personal Data and User Modelling in Tourism" at the ENTER 2013 conference.

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  • Following the best practices in Ontology engineering we use existing vocabularies.
  • The weighted interest includes
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    • 1. Personal Data and User Modelling in Tourism Ioannis Stavrakantonakis STI Innsbruck University of Innsbruck, Austria ENTER 2013, Innsbruck 1
    • 2. Data, data.. more data!©Google, http://www.google.com/about/datacenters Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 2
    • 3. Social Web data• Facebook: One billion monthly active users, (https://www.facebook.com/facebook, October 2012)• Twitter: Summer Olympics ‘12 in London generated 150 million Tweets (https://2012.twitter.com/en/pulse-of-the-planet.html)• Foursquare: A half billion check-ins the last 3 months, (http://blog.foursquare.com, Jan 17th 2013) Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 3
    • 4. Recommendation systemsWhere should you eat for dinner tonight? What should you visit in Innsbruck?Where to go for a drink? Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 4
    • 5. Recommenders examplesNara.me asks user’s taste about:• types of restaurants• cuisines• location• 2 restaurants in the city Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 5
    • 6. Recommenders examples (cont.)SUPE by Toyota[1]:• In-vehicle navigation system recommender• Collects driver preferences to provide personalised POI search results to the driver• Uses GPS logs (historical data) Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 6
    • 7. The ProblemPersonal Data in Social Web• Data is contained within disparate silosRecommendation systems Everywhere and nowhere,• User models are trapped in David Simonds, Economist 2008* proprietary data warehouses• User model properties are not standardised in various domains [4] *http://www.economist.com/business/displaystory.cfm?story_id=10880936 Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 7
    • 8. Research Questions• How could we bring closer the personal data of the users and the recommendation systems?• How could we lower the borders among the recommenders?• Which personal data could be used by the recommenders in tourism? Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 8
    • 9. Our Approach• Open User Model – Capturing personal data from the Social Web – Specific for tourism – Enable both personalisation systems and travellers to benefit – Based on existing ontologies reuse Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 9
    • 10. Our Approach (cont.)• Aims to – facilitate the extraction of personal data from Social Web; – facilitate the interoperability among recommenders in the tourism domain; – enable the users to consume personalised services from the data that they have already shared in the Social Web. Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 10
    • 11. Related work in user modellingGUMO [6], SWUM[5]: – Cover any attribute of a user model for the Social Web – Not specific for any domain – Aim to allow an easy data sharing between applicationsMypes[3]: – Cross-system user modelling Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 11
    • 12. The methodology• Define the attributes of the user model. [2] – Basic user characteristics – Interests – Time dimension – Historical data (e.g. visited places) – User’s wishes Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 12
    • 13. The methodology (cont.)• Following a bottom-up methodology 1. study the specifications of social networks (i.e. Facebook & Foursquare) 2. extract user attributes related to tourism from the data models 3. map the extracted attributes Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 13
    • 14. User Model for TourismUser model aspects Facebook Foursquare CommentsPersonal information   Name, EmailMarital status   Spouse, ChildrenHometown  Current city  Visited POIs   Coordinates, Name, CategoryPOIs to Explore   POIs saved in ToDo listsInterests  Liked locations  Activities   Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 14
    • 15. User Model for Tourism (cont.)• Reuse of existing vocabularies – FOAF (http://xmlns.com/foaf/spec/) • describe basic information about people • describe Internet accounts, web-based activities – Geo (http://www.w3.org/2003/01/geo/) • information about spatially-located things – Wi (http://xmlns.notu.be/wi/) • describe that a person prefers one thing to another Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 15
    • 16. User Model for Tourism (cont.) foaf:knows foaf:Person wi:preference foaf:name umt:POI foaf:mbox umt:namewi:WeightedInterest foaf:account umt:hasToDo umt:category umt:timestamp umt:hasVisited geo:lat geo:long umt:hasHometown umt:Location umt:hasCurrentLocation umt:name Property geo:lat Subclass of umt:likesLocation geo:long Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 16
    • 17. Conclusion• The data models of Social Networks are very similar regarding the visited places of the users.• Personal data in the Social Web contain reusable information for recommendation in the tourism domain.• An approach for the exploitation of this data in tourism. Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 17
    • 18. Future steps• Finalisation of the UMT model• Exploitation of the Google Latitude data• Evaluation of the approach and model Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 18
    • 19. Questions?ioannis.stavrakantonakis@sti2.atistavrak.com@istavrak Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 19
    • 20. References1. Parundekar, R., & Oguchi, K. (2012). Learning Driver Preferences of POIs Using a Semantic Web Knowledge System. The Semantic Web: Research and Applications.2. Kang, E., Kim, H., & Cho, J. (2006). Personalization method for tourist point of interest (POI) recommendation. Knowledge-Based Intelligent Information and Engineering Systems.3. Abel, F., Herder, E., Houben, G., Henze, N., & Krause, D. (2011). Cross-system user modeling and personalization on the social web. UMUAI Journal.4. Aroyo, L., & Houben, G. (2010). User modeling and adaptive Semantic Web. Semantic Web Journal.5. Plumbaum, T., Wu, S., De Luca, E., & Albayrak, S. (2011). User Modeling for the Social Semantic Web. Proceedings of SPIM 2011.6. Heckmann, D., Schwarzkopf, E., Mori, J., Dengler, D., & Kröner, A. (2007). The user model and context ontology GUMO revisited for future Web 2.0 extensions. Contexts and Ontologies: Representation and Reasoning. Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 20