Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism

Detalhes bibliográficos
Autor(a) principal: Silva, David Luís Correia da
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/24146
Resumo: In recent years, the tourism industry has witnessed substantial growth, thanks to the pro liferation of digital technology and online platforms. Tourists now have greater access to information and the ability to make informed travel decisions. However, the abundance of available information often leaves tourists overwhelmed when selecting points of inter est (POI) that align with their preferences. Recommender Systems (RS) have emerged as a solution, personalising recommendations based on tourist behaviour, social networks, and contextual factors. To enhance RS efficacy, researchers have begun exploring the integration of psychological factors, such as personality traits. Yet, to meet the demands of modern tourists, a robust knowledge base, such as a tourist attractions ontology, is essential for seamless and rapid matching of tourist characteristics and preferences with available POI. With that in mind, this project aims to enhance a Group Recommender System (GRS) prototype, GrouPlanner, by creating a robust tourist attractions ontology. This ontology will facilitate rapid and accurate matching of points of interest with tourists’ character istics, including personality, preferences, and demographic data, ultimately improving POI recommendations. First, there needs to be an understanding of the personality of tourists and how it influences their choices when it comes to picking the best point of interest based on their personality. With that knowledge acquired, it is time to choose a way to represent this knowledge in the form of an ontology. In this project, the Protégé ontology editor was used to design the ontology and the rela tionships between the tourists’ personality and the points of interest. After designing the ontology, it had to be converted to a database so the Grouplanner system could access it. So, to do that, a solution was designed to integrate the designed ontology in a triple store data base, in this case, Apache Fuseki. With the database implemented, several tests were made to verify if the database would give the recommended points of interests based on the tourists’ preferences. This tests were later analysed.
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spelling Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for TourismPoint of interestRecommendation systemTourismOntologyPersonalityDomínio/Área Científica::Engenharia e TecnologiaIn recent years, the tourism industry has witnessed substantial growth, thanks to the pro liferation of digital technology and online platforms. Tourists now have greater access to information and the ability to make informed travel decisions. However, the abundance of available information often leaves tourists overwhelmed when selecting points of inter est (POI) that align with their preferences. Recommender Systems (RS) have emerged as a solution, personalising recommendations based on tourist behaviour, social networks, and contextual factors. To enhance RS efficacy, researchers have begun exploring the integration of psychological factors, such as personality traits. Yet, to meet the demands of modern tourists, a robust knowledge base, such as a tourist attractions ontology, is essential for seamless and rapid matching of tourist characteristics and preferences with available POI. With that in mind, this project aims to enhance a Group Recommender System (GRS) prototype, GrouPlanner, by creating a robust tourist attractions ontology. This ontology will facilitate rapid and accurate matching of points of interest with tourists’ character istics, including personality, preferences, and demographic data, ultimately improving POI recommendations. First, there needs to be an understanding of the personality of tourists and how it influences their choices when it comes to picking the best point of interest based on their personality. With that knowledge acquired, it is time to choose a way to represent this knowledge in the form of an ontology. In this project, the Protégé ontology editor was used to design the ontology and the rela tionships between the tourists’ personality and the points of interest. After designing the ontology, it had to be converted to a database so the Grouplanner system could access it. So, to do that, a solution was designed to integrate the designed ontology in a triple store data base, in this case, Apache Fuseki. With the database implemented, several tests were made to verify if the database would give the recommended points of interests based on the tourists’ preferences. This tests were later analysed.Nos anos mais recentes, a indústria do turismo presenciou um crescimento substancial dev ido à tecnologia digital e plataformas online. Cada vez mais, os turistas têm acesso a uma abundância de informação que influencia a habilidade de tomar decisões sobre viajar. No entanto, esta informação pode complicar a seleção dos pontos de interesse que alinhem com as preferências dos turistas. Para combater isso, sistemas de recomendação (SR) emergi ram como uma solução, personalizando as recomendações com base no comportamento do turista, redes socias e outros fatores. Para aumentar a eficácia destes sistemas, os investi gadores começaram a explorar a possibilidade de integração com fatores psicológicos, como traços de personalidade. Apesar disso, para cumprir as exigências dos turistas modernos, uma base de conhecimento robusta, como uma ontologia de atrações turísticas, é essencial para, de forma eficaz e eficiente, corresponder as características dos turistas com os pontos de interesse disponíveis. Com isso em mente, este projeto tem como objetivo melhorar um protótipo de um sistema de recomendação (GrouPlanner), criando uma ontologia robusta de atrações turísticas. Essa ontologia facilitará a correspondência rápida e precisa de pontos de interesse com as car acterísticas dos turistas, incluindo a sua personalidade e as suas preferências, melhorando assim as recomendações de pontos de interesse. Em primeiro lugar, é necessário compreender a personalidade dos turistas e como ela influ encia as suas escolhas ao selecionar o melhor ponto de interesse com base na sua person alidade. Com esse ponto adquirido, é necessário escolher uma maneira de representar esse conhecimento na forma de uma ontologia. Neste projeto, o editor de ontologias Protégé foi utilizado para projetar a ontologia e as relações entre a personalidade dos turistas e os pontos de interesse. Após a construção da ontologia, foi necessário convertê-la numa base de dados para que o sistema Grouplanner pudesse ter acesso. Para isso, foi desenhada uma solução para integrar a ontologia projetada numa base de dados "triple store", neste caso, o Apache Fuseki. Com a base de dados implementada, foram realizados vários testes para verificar se esta forneceria os pontos de interesse recomendados com base nas preferências dos turistas. Esses testes foram depois analisados.Marreiros, Maria Goreti CarvalhoRepositório Científico do Instituto Politécnico do PortoSilva, David Luís Correia da2023-12-12T15:53:00Z2023-11-172023-11-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.22/24146TID:203413296enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-20T01:56:24Zoai:recipp.ipp.pt:10400.22/24146Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:42:21.803037Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
title Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
spellingShingle Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
Silva, David Luís Correia da
Point of interest
Recommendation system
Tourism
Ontology
Personality
Domínio/Área Científica::Engenharia e Tecnologia
title_short Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
title_full Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
title_fullStr Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
title_full_unstemmed Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
title_sort Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
author Silva, David Luís Correia da
author_facet Silva, David Luís Correia da
author_role author
dc.contributor.none.fl_str_mv Marreiros, Maria Goreti Carvalho
Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Silva, David Luís Correia da
dc.subject.por.fl_str_mv Point of interest
Recommendation system
Tourism
Ontology
Personality
Domínio/Área Científica::Engenharia e Tecnologia
topic Point of interest
Recommendation system
Tourism
Ontology
Personality
Domínio/Área Científica::Engenharia e Tecnologia
description In recent years, the tourism industry has witnessed substantial growth, thanks to the pro liferation of digital technology and online platforms. Tourists now have greater access to information and the ability to make informed travel decisions. However, the abundance of available information often leaves tourists overwhelmed when selecting points of inter est (POI) that align with their preferences. Recommender Systems (RS) have emerged as a solution, personalising recommendations based on tourist behaviour, social networks, and contextual factors. To enhance RS efficacy, researchers have begun exploring the integration of psychological factors, such as personality traits. Yet, to meet the demands of modern tourists, a robust knowledge base, such as a tourist attractions ontology, is essential for seamless and rapid matching of tourist characteristics and preferences with available POI. With that in mind, this project aims to enhance a Group Recommender System (GRS) prototype, GrouPlanner, by creating a robust tourist attractions ontology. This ontology will facilitate rapid and accurate matching of points of interest with tourists’ character istics, including personality, preferences, and demographic data, ultimately improving POI recommendations. First, there needs to be an understanding of the personality of tourists and how it influences their choices when it comes to picking the best point of interest based on their personality. With that knowledge acquired, it is time to choose a way to represent this knowledge in the form of an ontology. In this project, the Protégé ontology editor was used to design the ontology and the rela tionships between the tourists’ personality and the points of interest. After designing the ontology, it had to be converted to a database so the Grouplanner system could access it. So, to do that, a solution was designed to integrate the designed ontology in a triple store data base, in this case, Apache Fuseki. With the database implemented, several tests were made to verify if the database would give the recommended points of interests based on the tourists’ preferences. This tests were later analysed.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-12T15:53:00Z
2023-11-17
2023-11-17T00:00:00Z
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