Artificial Intelligence techniques in Tourism at URV
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Tourism applications of Artificial Intelligence techniques
Dr. Antonio Moreno, ITAKA research group, URV
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ITAKA – Basic research lines
Multi-agent systemsOntology LearningInformation ExtractionAutomated clusteringIntelligent decision support systemsPreference managementPrivacy protection
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ITAKA – Basic research lines
Multi-agent systemsOntology LearningInformation ExtractionAutomated clusteringIntelligent decision support systemsPreference managementPrivacy protection
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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
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Turist@: agent-based personalised recommendation of cultural activities
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Main features of Turist@
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Main features of Turist@• Dynamic management of user profile
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Main features of Turist@• Dynamic management of user profile
– Initial questionnaire
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Main features of Turist@• Dynamic management of user profile
– Initial questionnaire– Update after explicit evaluation
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Main features of Turist@• Dynamic management of user profile
– Initial questionnaire– Update after explicit evaluation– Update after user query
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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
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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
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Information ExtractionSpanish research project: DAMASK- Data mining algorithms with semantic knowledge (2010-2012)– Support from the
Scientific and Technological Park of Tourism and Leisure
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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]
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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
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Tourism ontology• Comprehensive coverage of touristic activities in
the region of Tarragona
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Recommendation techniques
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Recommendation techniques• Demographic information and travel motivations
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Recommendation techniques• Demographic information and travel motivations
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Recommendation techniques• Demographic information and travel motivations• User interaction with the system
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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
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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
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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
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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
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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
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Tourism applications of AI techniques
Dr. Antonio MorenoITAKA-Intelligent Tech. for Advanced
Knowledge AcquisitionComputer Science and Mathematics Dep.
Universitat Rovira i Virgili, Tarragonahttp://deim.urv.cat/~itaka