personal data and user modelling in tourism
DESCRIPTION
The presentation of the paper "Personal Data and User Modelling in Tourism" at the ENTER 2013 conference.TRANSCRIPT
1ENTER 2013, Innsbruck
Personal Data and User Modellingin Tourism
Ioannis Stavrakantonakis
STI Innsbruck University of Innsbruck, Austria
2Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck
Data, data.. more data!
©Google, http://www.google.com/about/datacenters
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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)
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Recommendation systems
Where should you eat for dinner tonight?
What should you visit in Innsbruck?
Where to go for a drink?
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Recommenders examples Nara.me asks user’s taste about:• types of restaurants• cuisines• location • 2 restaurants
in the city
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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)
Recommenders examples (cont.)
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The ProblemPersonal Data in Social Web• Data is contained within
disparate silos
Recommendation systems• User models are trapped in
proprietary data warehouses• User model properties are not standardised
in various domains [4] *http://www.economist.com/business/displaystory.cfm?story_id=10880936
Everywhere and nowhere, David Simonds, Economist 2008*
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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?
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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
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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.
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Related work in user modelling
GUMO [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
applications
Mypes[3]: – Cross-system user modelling
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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
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The methodology (cont.)
• Following a bottom-up methodology1. 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
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User Model for TourismUser model aspects Facebook Foursquare Comments
Personal information Name, EmailMarital status Spouse, ChildrenHometown Current city
Visited POIs Coordinates, Name, Category
POIs to Explore POIs saved in ToDo lists
Interests Liked locations Activities
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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
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User Model for Tourism (cont.)
umt:hasCurrentLocation
umt:hasHometown
wi:preference
umt:hasVisited
umt:hasToDo
foaf:knows
foaf:Person
foaf:namefoaf:mboxfoaf:account
umt:POI
umt:nameumt:categoryumt:timestampgeo:latgeo:long
wi:WeightedInterest
Property
Subclass of
umt:Location
umt:namegeo:latgeo:long
umt:likesLocation
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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.
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Future steps
• Finalisation of the UMT model
• Exploitation of the Google Latitude data
• Evaluation of the approach and model
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Questions?
[email protected]@istavrak
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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.