Application of diverse Artificial Technology techniques in the Tourism field at University Rovira i Virgili, Tarragona (ITAKA research group)

1. Tourism applications of
Artificial Intelligence techniques
Dr. Antonio Moreno, ITAKA research group, URV

2. ITAKA – Basic research lines
Multi-agent systems
Ontology Learning
Information Extraction
Automated clustering
Intelligent decision support systems
Preference management
Privacy protection

3. ITAKA – Basic research lines
Multi-agent systems
Ontology Learning
Information Extraction
Automated clustering
Intelligent decision support systems
Preference management
Privacy protection

4. Multi-agent systems
• Distributed computer systems, in which a
group of autonomous and proactive
intelligent agents communicate and
cooperate to solve a complex problem.
• Fields: Health Care and Tourism
• Work initiated within the AgentCities
European network, 2003-05

5. Turist@: agent-based personalised
recommendation of cultural activities

6. Main features of Turist@

7. Main features of Turist@
• Dynamic management of user profile

8. Main features of Turist@
• Dynamic management of user profile
– Initial questionnaire

9. Main features of Turist@
• Dynamic management of user profile
– Initial questionnaire
– Update after explicit evaluation

10. Main features of Turist@
• Dynamic management of user profile
– Initial questionnaire
– Update after explicit evaluation
– Update after user query

11. Main features of Turist@
• Dynamic management of user profile
– Initial questionnaire
– Update after explicit evaluation
– Update after user query
• Recommendation techniques
– Content-based
– Collaborative, based in clusters of users with similar demographic
data

12. Main features of Turist@
• Dynamic management of user profile
– Initial questionnaire
– Update after explicit evaluation
– Update after user query
• Recommendation techniques
– Content-based
– Collaborative, based in clusters of users with similar demographic
data
• User Agents running on mobile devices
– Pro-active and location-based recommendations

14. Information Extraction
Spanish research
project: DAMASK-
Data mining
algorithms with
semantic knowledge
(2010-2012)
– Support from the
Scientific and
Technological Park of
Tourism and Leisure

15. Basic steps in DAMASK
• Ontology-based extraction of relevant data from
structured, semi-structured and unstructured Web
resources, obtaining an attribute-value matrix
[touristic destinations from Wikipedia]
• Adaptation of traditional clustering methods to
create classifications (trees and partitions) using
semantic information
• Test the practical applicability of the developed
methods in the area of Tourism, building a
prototype of a decision support system [2012]

19. SigTur/e-Destination
• Project developed in
cooperation with the
Scientific and
Technological Park for
Tourism and Leisure
(Vila-Seca), supported
by European funds
• Ontology-based
personalized
recommendation of
touristic activities in the
region of Tarragona

20. Tourism ontology
• Comprehensive coverage of touristic activities in
the region of Tarragona

21. Recommendation techniques

22. Recommendation techniques
• Demographic information and travel motivations

23. Recommendation techniques
• Demographic information and travel motivations

24. Recommendation techniques
• Demographic information and travel motivations
• User interaction with the system

25. Recommendation techniques
• Demographic information and travel motivations
• User interaction with the system
• Similarity of user with predefined frequent tourist
stereotypes
– British families with young children staying for two weeks
in a cheap hotel in Salou in August

26. Recommendation techniques
• Demographic information and travel motivations
• User interaction with the system
• Similarity of user with predefined frequent tourist
stereotypes
– British families with young children staying for two weeks
in a cheap hotel in Salou in August
• Classes of users with similar demographic data

27. Recommendation techniques
• Demographic information and travel motivations
• User interaction with the system
• Similarity of user with predefined frequent tourist
stereotypes
– British families with young children staying for two weeks
in a cheap hotel in Salou in August
• Classes of users with similar demographic data
• Classes of users with similar opinions

28. Recommendation techniques
• Demographic information and travel motivations
• User interaction with the system
• Similarity of user with predefined frequent tourist
stereotypes
– British families with young children staying for two weeks
in a cheap hotel in Salou in August
• Classes of users with similar demographic data
• Classes of users with similar opinions
Top-down and bottom-up propagation of preferences
through the ontology

32. Summary
• Many AI methodologies and tools (along
with ICTs) can succesfully be applied in the
Tourism field
– Knowledge representation and inference
through the use of ontologies
– Automated analysis of Tourism resources
– Intelligent and personalised recommender
systems or decision support tools
– Planning methods
– Aggregation techniques
– Dynamic management of user profiles

33. Tourism applications
of AI techniques
Dr. Antonio Moreno
ITAKA-Intelligent Tech. for Advanced
Knowledge Acquisition
Computer Science and Mathematics Dep.
Universitat Rovira i Virgili, Tarragona
http://deim.urv.cat/~itaka