Tourism applications of Artificial Intelligence techniques

Dr. Antonio Moreno, ITAKA research group, URV

ITAKA – Basic research lines

Multi-agent systemsOntology LearningInformation ExtractionAutomated clusteringIntelligent decision support systemsPreference managementPrivacy protection

ITAKA – Basic research lines

Multi-agent systemsOntology LearningInformation ExtractionAutomated clusteringIntelligent decision support systemsPreference managementPrivacy 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 personalised recommendation 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 research project: DAMASK- Data mining algorithms with semantic 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 MorenoITAKA-Intelligent Tech. for Advanced

Knowledge AcquisitionComputer Science and Mathematics Dep.

Universitat Rovira i Virgili, Tarragonahttp://deim.urv.cat/~itaka