Towards sustainability optimization in touristic route recommendation

dc.contributor.authorSánchez Pérez, Pabloes-ES
dc.contributor.authorBellogín, Alejandroes-ES
dc.date.accessioned2025-09-26T17:58:17Z
dc.date.available2025-09-26T17:58:17Z
dc.date.issued2024-07-16es_ES
dc.descriptionCapítulos en libroses_ES
dc.description.abstractWhile route recommendation plays a vital role in digital tourism services, conventional methods tend to be inherently complex, as they need to consider several constraints at once: user preferences, geographical and scheduling information, etc. Besides, recent interests from society and institutions have motivated a greater emphasis on sustainability across multiple sectors, including tourism. This work proposes to use reranking techniques as a plain and efficient method to generate personalized routes for users while considering sustainable goals in the procedure. We believe that these results could lead to new research directions regarding sustainable route recommendation techniques.es-ES
dc.description.abstractWhile route recommendation plays a vital role in digital tourism services, conventional methods tend to be inherently complex, as they need to consider several constraints at once: user preferences, geographical and scheduling information, etc. Besides, recent interests from society and institutions have motivated a greater emphasis on sustainability across multiple sectors, including tourism. This work proposes to use reranking techniques as a plain and efficient method to generate personalized routes for users while considering sustainable goals in the procedure. We believe that these results could lead to new research directions regarding sustainable route recommendation techniques.en-GB
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.format.mimetypeapplication/pdfes_ES
dc.identifier.urihttp://hdl.handle.net/11531/105308
dc.keywordsRecommender systems; route recommendation; sustainabilityes-ES
dc.keywordsRecommender systems; route recommendation; sustainabilityen-GB
dc.language.isoen-GBes_ES
dc.publisherUniversidad Complutense de Madrid (Madrid, España)es_ES
dc.rightses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.rights.uries_ES
dc.sourceLibro: 16th/19th International FLINS Conference on Computational Intelligence in Decision and Control and International Conference on Intelligence Systems and Knowledge Engineering - FLINS-ISKE 2024, Página inicial: 9-16, Página final:es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleTowards sustainability optimization in touristic route recommendationes_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES

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