Artificial Intelligence techniques in Tourism at URV

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

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Artificial Intelligence techniques in Tourism at URV

  1. 1. Tourism applications ofArtificial Intelligence techniques Dr. Antonio Moreno, ITAKA research group, URV
  2. 2. ITAKA – Basic research lines Multi-agent systems Ontology Learning Information Extraction Automated clustering Intelligent decision support systems Preference management Privacy protection
  3. 3. ITAKA – Basic research lines Multi-agent systems Ontology Learning Information Extraction Automated clustering Intelligent decision support systems Preference management Privacy protection
  4. 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. 5. Turist@: agent-based personalisedrecommendation of cultural activities
  6. 6. Main features of Turist@
  7. 7. Main features of Turist@• Dynamic management of user profile
  8. 8. Main features of Turist@• Dynamic management of user profile – Initial questionnaire
  9. 9. Main features of Turist@• Dynamic management of user profile – Initial questionnaire – Update after explicit evaluation
  10. 10. Main features of Turist@• Dynamic management of user profile – Initial questionnaire – Update after explicit evaluation – Update after user query
  11. 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. 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
  13. 13. Information ExtractionSpanish researchproject: DAMASK-Data miningalgorithms withsemantic knowledge(2010-2012)– Support from the Scientific and Technological Park of Tourism and Leisure
  14. 14. 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]
  15. 15. 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
  16. 16. Tourism ontology• Comprehensive coverage of touristic activities in the region of Tarragona
  17. 17. Recommendation techniques
  18. 18. Recommendation techniques• Demographic information and travel motivations
  19. 19. Recommendation techniques• Demographic information and travel motivations
  20. 20. Recommendation techniques• Demographic information and travel motivations• User interaction with the system
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. Tourism applications of AI techniques Dr. Antonio Moreno ITAKA-Intelligent Tech. for Advanced Knowledge AcquisitionComputer Science and Mathematics Dep. Universitat Rovira i Virgili, Tarragona http://deim.urv.cat/~itaka

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