Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case

Esta tesis doctoral desarrolla un sistema de recomendaciones sensible al contexto orientado al turismo inteligente en el Páramo de Santurbán, bajo el paradigma de la Industria 4.0. Este ecosistema de alta montaña, estratégico por su biodiversidad y provisión de agua, enfrenta amenazas derivadas de a...

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Autores:: Flórez Franco, Marco Fidel

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2025

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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network_name_str	Repositorio UNAB
repository_id_str
dc.title.eng.fl_str_mv	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
dc.title.translated.spa.fl_str_mv	Sistema de recomendaciones sensible al contexto en el enfoque de tecnologías de la Industria 4.0 para aplicación en turismo inteligente en Santander: Caso Páramo de Santurbán
title	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
spellingShingle	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case Tourism recommendation Deep learning Hybrid recommendation method Context-aware recommender system Ontology-based knowledge Engineering Industry 4.0 Internet of things Service industry Ontologies (Information retrieval) Ecological tourism Ingeniería Industria 4.0 Internet de las cosas Industria de servicios Ontologías (Recuperación de información) Páramo (Santander, Colombia) Turismo ecológico Sistema de recomendación sensible al contexto Recomendación turística Aprendizaje profundo Método híbrido de recomendación Conocimiento basado en ontologías
title_short	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
title_full	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
title_fullStr	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
title_full_unstemmed	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
title_sort	Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case
dc.creator.fl_str_mv	Flórez Franco, Marco Fidel
dc.contributor.advisor.none.fl_str_mv	Carrillo Zambrano, Eduardo
dc.contributor.author.none.fl_str_mv	Flórez Franco, Marco Fidel
dc.contributor.cvlac.spa.fl_str_mv	Carrillo Zambrano, Eduardo [0000068780 Flórez Franco, Marco Fidel [0000907111]
dc.contributor.googlescholar.spa.fl_str_mv	Carrillo Zambrano, Eduardo [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv	Carrillo Zambrano, Eduardo [0000-0002-0868-940X] Flórez Franco, Marco Fidel [0000-0002-2386-5117]
dc.contributor.scopus.spa.fl_str_mv	Carrillo Zambrano, Eduardo [15622921600] Flórez Franco, Marco Fidel [57191196981]
dc.contributor.researchgate.spa.fl_str_mv	Carrillo Zambrano, Eduardo [Eduardo_Carrillo_Zambrano]
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigación en Ciencias Aplicadas - GINCAP
dc.contributor.apolounab.spa.fl_str_mv	Carrillo Zambrano, Eduardo [eduardo-carrillo-zambrano]
dc.subject.keywords.spa.fl_str_mv	Tourism recommendation Deep learning Hybrid recommendation method Context-aware recommender system Ontology-based knowledge Engineering Industry 4.0 Internet of things Service industry Ontologies (Information retrieval) Ecological tourism
topic	Tourism recommendation Deep learning Hybrid recommendation method Context-aware recommender system Ontology-based knowledge Engineering Industry 4.0 Internet of things Service industry Ontologies (Information retrieval) Ecological tourism Ingeniería Industria 4.0 Internet de las cosas Industria de servicios Ontologías (Recuperación de información) Páramo (Santander, Colombia) Turismo ecológico Sistema de recomendación sensible al contexto Recomendación turística Aprendizaje profundo Método híbrido de recomendación Conocimiento basado en ontologías
dc.subject.lemb.spa.fl_str_mv	Ingeniería Industria 4.0 Internet de las cosas Industria de servicios Ontologías (Recuperación de información) Páramo (Santander, Colombia) Turismo ecológico
dc.subject.proposal.spa.fl_str_mv	Sistema de recomendación sensible al contexto Recomendación turística Aprendizaje profundo Método híbrido de recomendación Conocimiento basado en ontologías
description	Esta tesis doctoral desarrolla un sistema de recomendaciones sensible al contexto orientado al turismo inteligente en el Páramo de Santurbán, bajo el paradigma de la Industria 4.0. Este ecosistema de alta montaña, estratégico por su biodiversidad y provisión de agua, enfrenta amenazas derivadas de actividades extractivas y de una gestión turística inadecuada. La propuesta integra ontologías y aprendizaje profundo en un modelo híbrido capaz de operar en entornos con conectividad limitada, proporcionando recomendaciones personalizadas alineadas con objetivos de conservación y desarrollo local. Estructurada como un compendio de artículos, la investigación aborda la identificación y caracterización de actores, el desarrollo de una ontología para turismo sostenible en áreas protegidas y la implementación de algoritmos de recomendación basados en inteligencia artificial para la identificación de especies y la gestión contextual del visitante. Los resultados, con métricas de alto rendimiento y evaluaciones positivas en campo, evidencian la utilidad, pertinencia y diversidad de las sugerencias. Se demuestra que la integración de tecnologías semánticas y aprendizaje automático en sistemas de recomendación fortalece la conservación, optimiza la experiencia turística y genera oportunidades económicas sostenibles, proponiendo un modelo replicable en otros parques naturales con proyecciones de escalabilidad mediante aprendizaje federado, integración de datos ambientales en tiempo real y colaboración interinstitucional.
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-08-12T13:18:36Z
dc.date.available.none.fl_str_mv	2025-08-12T13:18:36Z
dc.date.issued.none.fl_str_mv	2025-07-11
dc.type.eng.fl_str_mv	Thesis
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dc.type.local.spa.fl_str_mv	Tesis
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dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Stronza, A.L., Hunt, C.A., Fitzgerald, L.A.: Ecotourism for conservation? Annu Rev Environ Resour. 44, 229–253 (2019) Samal, R., Dash, M.: Ecotourism, biodiversity conservation and livelihoods: Understanding the convergence and divergence. International Journal of Geoheritage and Parks. 11, 1–20 (2023). https://doi.org/https://doi.org/10.1016/j.ijgeop.2022.11.001 Acevedo-Tarazona, Á.: Bucaramanga, entre la sobreexplotación minera o la preservación del agua en el páramo de Santurbán. Entramado. 16, 112–124 (2020) Méndez-Villamizar, R., Mejía-Jerez, A., Acevedo-Tarazona, Á.: Territorialidades y representaciones sociales superpuestas en la dicotomía agua vs. oro: el conflicto socioambiental por minería industrial en el páramo de Santurbán. Territorios. 150–174 (2020) Baloch, Q.B., Shah, S.N., Iqbal, N., Sheeraz, M., Asadullah, M., Mahar, S., Khan, A.U.: Impact of tourism development upon environmental sustainability: a suggested framework for sustainable ecotourism. Environmental Science and Pollution Research. 30, 5917–5930 (2023) Harianto, S.P., Walid Masruri, N., Winarno, G.D., Tsani, M.K., Santoso, T.: Development strategy for ecotourism management based on feasibility analysis of tourist attraction objects and perception of visitors and local communities. Biodiversitas. 21, 689–698 (2020) Wondirad, A., Tolkach, D., King, B.: Stakeholder collaboration as a major factor for sustainable ecotourism development in developing countries. Tour Manag. 78, 104024 (2020) Bulchand-Gidumal, J.: Impact of artificial intelligence in travel, tourism, and hospitality. In: Handbook of e-Tourism. pp. 1943–1962. Springer (2022) Ali, Q., Yaseen, M.R., Anwar, S., Makhdum, M.S.A., Khan, M.T.I.: The impact of tourism, renewable energy, and economic growth on ecological footprint and natural resources: A panel data analysis. Resources Policy. 74, 102365 (2021) London, S., Rojas, M.L., Candias, K.N.: Sustainable Tourism: A Model for Growth Using Natural Resources. Ensayos de Economía. 31, 158–177 (2021) Balakrishnan, J., Dwivedi, Y.K., Malik, F.T., Baabdullah, A.M.: Role of smart tourism technology in heritage tourism development. Journal of Sustainable Tourism. 31, 2506–2525 (2023) Alsahafi, R., Alzahrani, A., Mehmood, R.: Smarter sustainable tourism: data-driven multi-perspective parameter discovery for autonomous design and operations. Sustainability. 15, 4166 (2023) Lee, C.-I., Hsia, T.-C., Hsu, H.-C., Lin, J.-Y.: Ontology-based tourism recommendation system. In: 2017 4th International Conference on Industrial Engineering and Applications (ICIEA). pp. 376–379. IEEE (2017) Richardson, R.B.: The role of tourism in sustainable development. In: Oxford research encyclopedia of environmental science (2021) Rahmadian, E., Feitosa, D., Virantina, Y.: Digital twins, big data governance, and sustainable tourism. Ethics Inf Technol. 25, 61 (2023) Ko, H., Lee, S., Park, Y., Choi, A.: A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics (Basel). 11, 141 (2022) Alfaifi, Y.H.: Recommender Systems Applications: Data Sources, Features, and Challenges. Information. 15, 660 (2024) Elahi, M., Beheshti, A., Goluguri, S.R.: Recommender systems: Challenges and opportunities in the age of big data and artificial intelligence. Data Science and Its Applications. 15–39 (2021) Zhang, Q., Lu, J., Jin, Y.: Artificial intelligence in recommender systems. Complex & Intelligent Systems. 7, 439–457 (2021) Castellanos, G., Cardinale, Y., Roose, P.: Context-aware and Ontology-based Recommender System for E-tourism. In: ICSOFT (2021) Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook. pp. 217–253. Springer (2010) Bahramian, Z., Ali Abbaspour, R., Claramunt, C.: A context-aware tourism recommender system based on a spreading activation method. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 42, 333–339 (2017) Abbas, A., Zhang, L., Khan, S.U.: A survey on context-aware recommender systems based on computational intelligence techniques. Computing. 97, 667–690 (2015) Dalla Vecchia, A., Migliorini, S., Quintarelli, E., Gambini, M., Belussi, A.: Promoting sustainable tourism by recommending sequences of attractions with deep reinforcement learning. Information Technology & Tourism. 1–36 (2024) Cañas, H., Mula, J., Díaz-Madroñero, M., Campuzano-Bolarín, F.: Implementing industry 4.0 principles. Comput Ind Eng. 158, 107379 (2021) Ozturk, H.M.: Technological Developments: Industry 4.0 and its effect on the tourism sector. In: Handbook of research on smart technology applications in the tourism industry. pp. 205–228. IGI Global (2020) Pinto, A., Cardinale, Y., Dongo, I., Ticona-Herrera, R.: An ontology for modeling cultural heritage knowledge in urban tourism. IEEE Access. 10, 61820–61842 (2022) Virmani, C., Sinha, S., Khatri, S.K.: Unified ontology for data integration for tourism sector. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS). pp. 152–156. IEEE (2017) Prantner, K., Ding, Y., Luger, M., Yan, Z., Herzog, C.: Tourism ontology and semantic management system: state-of-the-arts analysis. In: IADIS international conference WWW/Internet. pp. 111–115 (2007) Fodor, O., Werthner, H.: Harmonise: a step toward an interoperable e-tourism marketplace. International Journal of Electronic Commerce. 9, 11–39 (2005) Abadi, M.: TensorFlow: learning functions at scale. In: Proceedings of the 21st ACM SIGPLAN international conference on functional programming. p. 1 (2016) Wang, R., Luo, J., Huang, S.S.: Developing an artificial intelligence framework for online destination image photos identification. Journal of Destination Marketing & Management. 18, 100512 (2020) Shrimali, S.: Plantifyai: a novel convolutional neural network based mobile application for efficient crop disease detection and treatment. Procedia Comput Sci. 191, 469–474 (2021)
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dc.coverage.spatial.spa.fl_str_mv	Páramo de Santurbán (Santander, Colombia)
dc.coverage.temporal.spa.fl_str_mv	2022-2024
dc.coverage.campus.spa.fl_str_mv	UNAB Campus Bucaramanga
dc.publisher.grantor.spa.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv	Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv	Doctorado en Ingeniería
dc.publisher.programid.none.fl_str_mv	DING-1502
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spelling	Carrillo Zambrano, Eduardo0af7e78d-2c4d-4652-a7d8-606c9e3f667eFlórez Franco, Marco Fidel5e68dcfd-cd39-4524-b485-3f620172361cCarrillo Zambrano, Eduardo [0000068780Flórez Franco, Marco Fidel [0000907111]Carrillo Zambrano, Eduardo [es&oi=ao]Carrillo Zambrano, Eduardo [0000-0002-0868-940X]Flórez Franco, Marco Fidel [0000-0002-2386-5117]Carrillo Zambrano, Eduardo [15622921600]Flórez Franco, Marco Fidel [57191196981]Carrillo Zambrano, Eduardo [Eduardo_Carrillo_Zambrano]Grupo de Investigación en Ciencias Aplicadas - GINCAPCarrillo Zambrano, Eduardo [eduardo-carrillo-zambrano]Páramo de Santurbán (Santander, Colombia)2022-2024UNAB Campus Bucaramanga2025-08-12T13:18:36Z2025-08-12T13:18:36Z2025-07-11http://hdl.handle.net/20.500.12749/30740instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coEsta tesis doctoral desarrolla un sistema de recomendaciones sensible al contexto orientado al turismo inteligente en el Páramo de Santurbán, bajo el paradigma de la Industria 4.0. Este ecosistema de alta montaña, estratégico por su biodiversidad y provisión de agua, enfrenta amenazas derivadas de actividades extractivas y de una gestión turística inadecuada. La propuesta integra ontologías y aprendizaje profundo en un modelo híbrido capaz de operar en entornos con conectividad limitada, proporcionando recomendaciones personalizadas alineadas con objetivos de conservación y desarrollo local. Estructurada como un compendio de artículos, la investigación aborda la identificación y caracterización de actores, el desarrollo de una ontología para turismo sostenible en áreas protegidas y la implementación de algoritmos de recomendación basados en inteligencia artificial para la identificación de especies y la gestión contextual del visitante. Los resultados, con métricas de alto rendimiento y evaluaciones positivas en campo, evidencian la utilidad, pertinencia y diversidad de las sugerencias. Se demuestra que la integración de tecnologías semánticas y aprendizaje automático en sistemas de recomendación fortalece la conservación, optimiza la experiencia turística y genera oportunidades económicas sostenibles, proponiendo un modelo replicable en otros parques naturales con proyecciones de escalabilidad mediante aprendizaje federado, integración de datos ambientales en tiempo real y colaboración interinstitucional.Universidad de investigación y desarrollo-UDI1. INTRODUCTION 9 1.1 Background 10 1.2 Justification 11 1.3 Hypothesis 12 1.4 Objectives 12 1.4.1 General Objective: 12 1.4.2 Specific objectives: 12 1.5 Contribution of the Articles to the Thesis Development. 12 1.6 Articulating Scientific, Sustainable, and Educational Perspectives. 14 2. THEORETICAL FRAMEWORK 16 3. METHODOLOGICAL FRAMEWORK 18 3.1 Specific Methodological Approaches for Each Study 18 4. DISCUSSION FRAMEWORK 20 4.1 Evaluation of System Accuracy and User Experience 21 5. CONCLUSIONS 24 5.1 Research Question and Hypothesis Validation 24 5.2 Contribution to the Research Gap 24 5.3 Empirical Validation and User Feedback 24 5.3.1 Response to the Research Question 24 5.4 Integration of Conservation and Tourism 25 5.5 Comparative Analysis with Related Works 26 5.6 Final Recommendations and Research Projection 27 5.6.1 Optimization and Scalability of the System 27 5.6.2 Continuous Refinement and New Data Sources and Technologies 28 5.6.3 Impact Evaluation, Sustainability, and Inter-Institutional Collaboration 28 5.7 Synthesis and Closure of the Gap 29 6. IMPLEMENTATION AND REPLICATION GUIDE FOR THE RECOMMENDATION SYSTEM IN OTHER NATURAL PARKS OF COLOMBIA 29 7. BIBLIOGRAPHIC REFERENCES 33 8. REFERENCES 34DoctoradoThis doctoral thesis develops a context-sensitive recommendation system aimed at intelligent tourism in the Santurbán Páramo, within the framework of the Industry 4.0 paradigm. This high-mountain ecosystem, strategic for its biodiversity and water provision, faces threats from extractive activities and inadequate tourism management. The proposed approach integrates ontologies and deep learning into a hybrid model capable of operating in environments with limited connectivity, delivering personalized recommendations aligned with conservation objectives and local development goals. Structured as a compendium of articles, the research addresses the identification and characterization of stakeholders, the development of an ontology for sustainable tourism in protected areas, and the implementation of artificial intelligence–based recommendation algorithms for species identification and contextual visitor management. The results, with high-performance metrics and positive field evaluations, highlight the usefulness, relevance, and diversity of the suggestions. The study demonstrates that integrating semantic technologies and machine learning into recommendation systems strengthens conservation, optimizes the tourist experience, and generates sustainable economic opportunities, proposing a replicable model for other natural parks with scalability projections through federated learning, real-time environmental data integration, and inter-institutional collaboration.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo caseSistema de recomendaciones sensible al contexto en el enfoque de tecnologías de la Industria 4.0 para aplicación en turismo inteligente en Santander: Caso Páramo de SanturbánThesisinfo:eu-repo/semantics/doctoralThesisTesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TDDoctorado en IngenieríaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaDoctorado en IngenieríaDING-1502Tourism recommendationDeep learningHybrid recommendation methodContext-aware recommender systemOntology-based knowledgeEngineeringIndustry 4.0Internet of thingsService industryOntologies (Information retrieval)Ecological tourismIngenieríaIndustria 4.0Internet de las cosasIndustria de serviciosOntologías (Recuperación de información)Páramo (Santander, Colombia)Turismo ecológicoSistema de recomendación sensible al contextoRecomendación turísticaAprendizaje profundoMétodo híbrido de recomendaciónConocimiento basado en ontologíasStronza, A.L., Hunt, C.A., Fitzgerald, L.A.: Ecotourism for conservation? Annu Rev Environ Resour. 44, 229–253 (2019)Samal, R., Dash, M.: Ecotourism, biodiversity conservation and livelihoods: Understanding the convergence and divergence. International Journal of Geoheritage and Parks. 11, 1–20 (2023). https://doi.org/https://doi.org/10.1016/j.ijgeop.2022.11.001Acevedo-Tarazona, Á.: Bucaramanga, entre la sobreexplotación minera o la preservación del agua en el páramo de Santurbán. Entramado. 16, 112–124 (2020)Méndez-Villamizar, R., Mejía-Jerez, A., Acevedo-Tarazona, Á.: Territorialidades y representaciones sociales superpuestas en la dicotomía agua vs. oro: el conflicto socioambiental por minería industrial en el páramo de Santurbán. Territorios. 150–174 (2020)Baloch, Q.B., Shah, S.N., Iqbal, N., Sheeraz, M., Asadullah, M., Mahar, S., Khan, A.U.: Impact of tourism development upon environmental sustainability: a suggested framework for sustainable ecotourism. Environmental Science and Pollution Research. 30, 5917–5930 (2023)Harianto, S.P., Walid Masruri, N., Winarno, G.D., Tsani, M.K., Santoso, T.: Development strategy for ecotourism management based on feasibility analysis of tourist attraction objects and perception of visitors and local communities. Biodiversitas. 21, 689–698 (2020)Wondirad, A., Tolkach, D., King, B.: Stakeholder collaboration as a major factor for sustainable ecotourism development in developing countries. Tour Manag. 78, 104024 (2020)Bulchand-Gidumal, J.: Impact of artificial intelligence in travel, tourism, and hospitality. In: Handbook of e-Tourism. pp. 1943–1962. Springer (2022)Ali, Q., Yaseen, M.R., Anwar, S., Makhdum, M.S.A., Khan, M.T.I.: The impact of tourism, renewable energy, and economic growth on ecological footprint and natural resources: A panel data analysis. Resources Policy. 74, 102365 (2021)London, S., Rojas, M.L., Candias, K.N.: Sustainable Tourism: A Model for Growth Using Natural Resources. Ensayos de Economía. 31, 158–177 (2021)Balakrishnan, J., Dwivedi, Y.K., Malik, F.T., Baabdullah, A.M.: Role of smart tourism technology in heritage tourism development. Journal of Sustainable Tourism. 31, 2506–2525 (2023)Alsahafi, R., Alzahrani, A., Mehmood, R.: Smarter sustainable tourism: data-driven multi-perspective parameter discovery for autonomous design and operations. Sustainability. 15, 4166 (2023)Lee, C.-I., Hsia, T.-C., Hsu, H.-C., Lin, J.-Y.: Ontology-based tourism recommendation system. In: 2017 4th International Conference on Industrial Engineering and Applications (ICIEA). pp. 376–379. IEEE (2017)Richardson, R.B.: The role of tourism in sustainable development. In: Oxford research encyclopedia of environmental science (2021)Rahmadian, E., Feitosa, D., Virantina, Y.: Digital twins, big data governance, and sustainable tourism. Ethics Inf Technol. 25, 61 (2023)Ko, H., Lee, S., Park, Y., Choi, A.: A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics (Basel). 11, 141 (2022)Alfaifi, Y.H.: Recommender Systems Applications: Data Sources, Features, and Challenges. Information. 15, 660 (2024)Elahi, M., Beheshti, A., Goluguri, S.R.: Recommender systems: Challenges and opportunities in the age of big data and artificial intelligence. Data Science and Its Applications. 15–39 (2021)Zhang, Q., Lu, J., Jin, Y.: Artificial intelligence in recommender systems. Complex & Intelligent Systems. 7, 439–457 (2021)Castellanos, G., Cardinale, Y., Roose, P.: Context-aware and Ontology-based Recommender System for E-tourism. In: ICSOFT (2021)Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook. pp. 217–253. 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Context-sensitive recommender system in the approach of industry 4.0 technologies for application in intelligent tourism in santander: santurbán paramo case

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