Artificial Intelligence techniques in Tourism at URV
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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)

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 Artificial Intelligence techniques in Tourism at URV Presentation Transcript

  • Tourism applications ofArtificial Intelligence techniques Dr. Antonio Moreno, ITAKA research group, URV
  • ITAKA – Basic research lines Multi-agent systems Ontology Learning Information Extraction Automated clustering Intelligent decision support systems Preference management Privacy protection
  • ITAKA – Basic research lines Multi-agent systems Ontology Learning Information Extraction Automated clustering Intelligent decision support systems Preference management Privacy protection
  • 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
  • Turist@: agent-based personalisedrecommendation of cultural activities
  • Main features of Turist@
  • Main features of Turist@• Dynamic management of user profile
  • Main features of Turist@• Dynamic management of user profile – Initial questionnaire
  • Main features of Turist@• Dynamic management of user profile – Initial questionnaire – Update after explicit evaluation
  • Main features of Turist@• Dynamic management of user profile – Initial questionnaire – Update after explicit evaluation – Update after user query
  • 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
  • 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
  • Information ExtractionSpanish researchproject: DAMASK-Data miningalgorithms withsemantic knowledge(2010-2012)– Support from the Scientific and Technological Park of Tourism and Leisure
  • 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]
  • 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
  • Tourism ontology• Comprehensive coverage of touristic activities in the region of Tarragona
  • Recommendation techniques
  • Recommendation techniques• Demographic information and travel motivations
  • Recommendation techniques• Demographic information and travel motivations
  • Recommendation techniques• Demographic information and travel motivations• User interaction with the system
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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