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

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 systems
Where 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 examples
Nara.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 Problem
Personal Data in Social Web
• Data is contained within
disparate silos
Recommendation 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
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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 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
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 Tourism
User model aspects Facebook Foursquare Comments
Personal information   Name, Email
Marital status   Spouse, Children
Hometown  
Current city  
Visited POIs   Coordinates, Name,
Category
POIs to Explore   POIs saved in ToDo
lists
Interests  
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:name
wi: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.
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18. Future steps
• Finalisation of the UMT model
• Exploitation of the Google Latitude data
• Evaluation of the approach and model
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19. Questions?
ioannis.stavrakantonakis@sti2.at
istavrak.com
@istavrak
Personal Data and User Modelling in Tourism - ENTER 2013, Innsbruck 19

20. References
1. 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