Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico
Los destinos turísticos emergentes enfrentan desafíos para posicionarse en el mercado global, especialmente en seguridad y calidad de la oferta. Riohacha (Departamento de la Guajira), es un distrito especial, turístico y cultural de Colombia que busca posicionarse como uno de los destinos turísticos...
- Autores:
-
BARRIOS BARRIOS, MAURICIO ANDRES
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2025
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/14284
- Acceso en línea:
- https://hdl.handle.net/11323/14284
https://repositorio.cuc.edu.co/
- Palabra clave:
- Sistema de recomendación
Turismo
Toma de decisiones multicriterio
Operadores de agregación
Modelado de preferencias
Lógica graduada
Recommender system
Tourism
Multi-criteria decision making
Aggregation operators
Preference modeling
Graded logic
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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| dc.title.spa.fl_str_mv |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| title |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| spellingShingle |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico Sistema de recomendación Turismo Toma de decisiones multicriterio Operadores de agregación Modelado de preferencias Lógica graduada Recommender system Tourism Multi-criteria decision making Aggregation operators Preference modeling Graded logic |
| title_short |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| title_full |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| title_fullStr |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| title_full_unstemmed |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| title_sort |
Sistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico |
| dc.creator.fl_str_mv |
BARRIOS BARRIOS, MAURICIO ANDRES |
| dc.contributor.advisor.none.fl_str_mv |
Valls Mateu, Aïda Acosta Coll, Melisa Escorcia Gutiérrez, José |
| dc.contributor.author.none.fl_str_mv |
BARRIOS BARRIOS, MAURICIO ANDRES |
| dc.contributor.corporatename.none.fl_str_mv |
Solano Barliza, Andrés David |
| dc.contributor.jury.none.fl_str_mv |
Gamarra Acosta, Margarita Rosa Ferrré Bergadà, Maria Barrios Barrios, Mauricio Andrés |
| dc.subject.proposal.spa.fl_str_mv |
Sistema de recomendación Turismo Toma de decisiones multicriterio Operadores de agregación Modelado de preferencias Lógica graduada |
| topic |
Sistema de recomendación Turismo Toma de decisiones multicriterio Operadores de agregación Modelado de preferencias Lógica graduada Recommender system Tourism Multi-criteria decision making Aggregation operators Preference modeling Graded logic |
| dc.subject.proposal.eng.fl_str_mv |
Recommender system Tourism Multi-criteria decision making Aggregation operators Preference modeling Graded logic |
| description |
Los destinos turísticos emergentes enfrentan desafíos para posicionarse en el mercado global, especialmente en seguridad y calidad de la oferta. Riohacha (Departamento de la Guajira), es un distrito especial, turístico y cultural de Colombia que busca posicionarse como uno de los destinos turísticos más importantes del país, sin embargo, la falta de datos para desarrollar modelos de recomendación de productos y servicios turísticos en Riohacha puede limitar su atractivo, afectando la competitividad y el flujo de visitantes. Esta tesis tiene como objetivo desarrollar un sistema informático de recomendación multicriterio, basado en lógica graduada de preferencias, que ofrezca a los turistas una guía segura y eficiente, mejorando la experiencia y promoviendo el crecimiento sostenible de destinos como Riohacha con pocos datos disponibles. La metodología empleada se basa en investigaciones básicas y aplicadas. La investigación se divide en cuatro fases: 1) Identificación y análisis de los criterios necesarios para la planificación y diseño de la solución tecnológica; 2) Definición de un sistema de recomendación personalizado utilizando técnicas de Inteligencia Artificial y toma de decisiones basada en múltiples criterios; 3) Desarrollo e implementación de una plataforma tecnológica basada en las preferencias del usuario, con el objetivo de fortalecer los indicadores de competitividad turística en Riohacha y 4) Evaluación de la solución tecnológica, donde se valora la funcionalidad y pertinencia de la solución implementada. El sistema recomendador que se propone en la tesis utiliza dos modelos basados en operadores de conjunción/disyunción graduada (GDC) siguiendo el método Logic Scoring of Preference (LSP) para mejorar la evaluación de hoteles y restaurantes. Estos modelos de agregación jerárquicos reflejan el razonamiento humano en la selección de estos servicios abordando múltiples requisitos simultáneamente, son versátiles y adaptables a diferentes destinos. Además, la tesis propone diversas funciones predefinidas que representan diferentes tipos de preferencias respecto a los valores de atributos contextuales, que permiten simplificar el proceso de toma de decisiones y personalizar la experiencia del usuario. Se han incorporado atributos relacionados con la seguridad turística y, como medida de garantía, se ha verificado que los servicios incluidos en el sistema de recomendación están debidamente registrados y validados por una entidad gubernamental encargada de certificar este tipo de servicios. Además, se han añadido recomendaciones y consejos de seguridad turística para que el turista pueda disfrutar de su recorrido de manera segura en el destino seleccionado. El procedimiento metodológico incluyó la recolección de datos, definición de jerarquías y selección de operadores, adaptados a las necesidades del usuario. Se introdujo el Food Specialty Interest Score (FSIS) para evaluar la idoneidad de los restaurantes según las preferencias culinarias del usuario. La validación inicial, realizada por expertos del destino turístico, mostró un recall de 1.0, destacando la efectividad del modelo. Posteriormente, el modelo fue evaluado por usuarios reales, quienes lo calificaron positivamente en cuanto a recomendaciones (86.42%) y facilidad para ingresar datos (77.78%), confirmando su eficacia y facilidad de uso. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-06-09T14:42:29Z |
| dc.date.available.none.fl_str_mv |
2025-06-09T14:42:29Z |
| dc.date.issued.none.fl_str_mv |
2025-04-04 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Doctorado |
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http://purl.org/coar/resource_type/c_db06 |
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Text |
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info:eu-repo/semantics/doctoralThesis |
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http://purl.org/redcol/resource_type/TD |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/14284 |
| dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.none.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/14284 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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spa |
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spa |
| dc.relation.references.none.fl_str_mv |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Valls Mateu, AïdaAcosta Coll, MelisaEscorcia Gutiérrez, JoséBARRIOS BARRIOS, MAURICIO ANDRESvirtual::1833-1Solano Barliza, Andrés DavidGamarra Acosta, Margarita RosaFerrré Bergadà, MariaBarrios Barrios, Mauricio Andrés2025-06-09T14:42:29Z2025-06-09T14:42:29Z2025-04-04https://hdl.handle.net/11323/14284Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Los destinos turísticos emergentes enfrentan desafíos para posicionarse en el mercado global, especialmente en seguridad y calidad de la oferta. Riohacha (Departamento de la Guajira), es un distrito especial, turístico y cultural de Colombia que busca posicionarse como uno de los destinos turísticos más importantes del país, sin embargo, la falta de datos para desarrollar modelos de recomendación de productos y servicios turísticos en Riohacha puede limitar su atractivo, afectando la competitividad y el flujo de visitantes. Esta tesis tiene como objetivo desarrollar un sistema informático de recomendación multicriterio, basado en lógica graduada de preferencias, que ofrezca a los turistas una guía segura y eficiente, mejorando la experiencia y promoviendo el crecimiento sostenible de destinos como Riohacha con pocos datos disponibles. La metodología empleada se basa en investigaciones básicas y aplicadas. La investigación se divide en cuatro fases: 1) Identificación y análisis de los criterios necesarios para la planificación y diseño de la solución tecnológica; 2) Definición de un sistema de recomendación personalizado utilizando técnicas de Inteligencia Artificial y toma de decisiones basada en múltiples criterios; 3) Desarrollo e implementación de una plataforma tecnológica basada en las preferencias del usuario, con el objetivo de fortalecer los indicadores de competitividad turística en Riohacha y 4) Evaluación de la solución tecnológica, donde se valora la funcionalidad y pertinencia de la solución implementada. El sistema recomendador que se propone en la tesis utiliza dos modelos basados en operadores de conjunción/disyunción graduada (GDC) siguiendo el método Logic Scoring of Preference (LSP) para mejorar la evaluación de hoteles y restaurantes. Estos modelos de agregación jerárquicos reflejan el razonamiento humano en la selección de estos servicios abordando múltiples requisitos simultáneamente, son versátiles y adaptables a diferentes destinos. Además, la tesis propone diversas funciones predefinidas que representan diferentes tipos de preferencias respecto a los valores de atributos contextuales, que permiten simplificar el proceso de toma de decisiones y personalizar la experiencia del usuario. Se han incorporado atributos relacionados con la seguridad turística y, como medida de garantía, se ha verificado que los servicios incluidos en el sistema de recomendación están debidamente registrados y validados por una entidad gubernamental encargada de certificar este tipo de servicios. Además, se han añadido recomendaciones y consejos de seguridad turística para que el turista pueda disfrutar de su recorrido de manera segura en el destino seleccionado. El procedimiento metodológico incluyó la recolección de datos, definición de jerarquías y selección de operadores, adaptados a las necesidades del usuario. Se introdujo el Food Specialty Interest Score (FSIS) para evaluar la idoneidad de los restaurantes según las preferencias culinarias del usuario. La validación inicial, realizada por expertos del destino turístico, mostró un recall de 1.0, destacando la efectividad del modelo. Posteriormente, el modelo fue evaluado por usuarios reales, quienes lo calificaron positivamente en cuanto a recomendaciones (86.42%) y facilidad para ingresar datos (77.78%), confirmando su eficacia y facilidad de uso.Emerging tourist destinations face challenges to position themselves in the global market, especially in terms of safety and quality of supply. Riohacha (Department of La Guajira), is a special tourist and cultural district of Colombia that seeks to position itself as one of the most important tourist destinations in the country, however, the lack of data to develop recommendation models for tourism products and services in Riohacha may limit its attractiveness, affecting competitiveness and the flow of visitors. This thesis aims to develop a multi-criteria recommendation computer system, based on graded logic of preferences, which offers tourists a safe and efficient guide, improving the experience and promoting sustainable growth of destinations like Riohacha with little data available. The methodology used is based on basic and applied research. The research is divided into four phases: 1) Identification and analysis of the criteria necessary for the planning and design of the technological solution; 2) Definition of a personalized recommendation system using Artificial Intelligence techniques and decision making based on multiple criteria; 3) Development and implementation of a technological platform based on user preferences, with the objective of strengthening tourism competitiveness indicators in Riohacha; and 4) Evaluation of the technological solution, where the functionality and relevance of the implemented solution is assessed. The recommender system proposed in the thesis uses two models based on graded conjunction/disjunction operators (GDC) following the Logic Scoring of Preference (LSP) method to improve the evaluation of hotels and restaurants. These hierarchical aggregation models reflect human reasoning in the selection of these services by addressing multiple requirements simultaneously, and are versatile and adaptable to different destinations. In addition, the thesis proposes several predefined functions that represent different types of preferences with respect to the values of contextual attributes, which allow simplifying the decision-making process and personalizing the user experience. Attributes related to tourism safety have been incorporated and, as a guarantee measure, it has been verified that the services included in the recommendation system are duly registered and validated by a government entity in charge of certifying this type of services. In addition, tourist safety recommendations and tips have been added so that tourists can enjoy their trip safely in the selected destination. The methodological procedure included data collection, definition of hierarchies and selection of operators, adapted to the user's needs. The Food Specialty Interest Score (FSIS) was introduced to evaluate the suitability of restaurants according to the user's culinary preferences. The initial validation, conducted by destination experts, showed a recall of 1.0, highlighting the effectiveness of the model. Subsequently, the model was evaluated by real users, who rated it positively in terms of recommendations (86.42%) and ease of data entry (77.78%), confirming its effectiveness and ease of use.Lista de tablas 17 -- Lista de figuras 19 -- Introducción 21 -- Motivación y/o problema 21 -- Objetivos 31 – Fundamentos 31 -- Aspectos inherentes a la competitividad turística 32 -- Tecnologías para el desarrollo de la solución 36 -- Fundamentos relativos a los sistemas de recomendación 43 -- Contribuciones y publicaciones 45 -- Organización del documento 53 -- Revisión de la literatura 55 -- Criterios de admisibilidad 55 -- Fuentes de información y estrategia de búsqueda 55 -- Selección de estudios y criterios de exclusión 56 -- Resultados 57 -- Análisis cienciométrico descriptivo 58 -- Categorización análisis técnico 61 -- Sistema de recomendación y gestión turística 62 -- Sistema de recomendación y tipología y técnicas 63 -- Sistemas de recomendación y aprendizaje 66 -- Revisión sobre sistemas de recomendación para hoteles y restaurantes 68 -- Recomendación de hoteles basada en técnicas mcda 68 -- Recomendación de restaurantes basada en técnicas de ml 69 -- Integración de ml y mcda para la recomendación de restaurantes 72 -- Recomendadores de restaurantes basados en mcda 74 -- Destinos turísticos emergentes con falta de datos 78 -- Sistema de recomendación híbrido en turismo 81 -- Conclusiones 82 -- Metodología 85 -- Fases de la investigación 85 -- Identificación y análisis de criterios 85 -- Definición de método para la construcción del modelo 86 -- Desarrollo e implementación del modelo basado en preferencias del usuario 87 -- Evaluación de la solución tecnológica 88 -- Técnicas de la investigación 89 -- Nueva metodología para el diseño de un modelo de sistema de recomendación para un destino turístico emergente 90 -- Selección del destino turístico de aplicación 90 -- Construcción del conjunto de datos 90 -- Modelización de preferencias en atributos 91 -- Definición de la arquitectura del modelo 91 -- Validación del modelo con expertos 92 -- Implementación y evaluación del modelo con usuarios reales 92 -- Conclusiones del capítulo 93 -- Resultados 95 -- El modelo de los hoteles 95 -- Atributos del modelo de sistema de recomendación para hoteles 95 -- Modelización de preferencias 96 -- Modelo general del sistema de recomendación de hoteles 98 -- Operadores de agregación 101 -- Modelo de recomendación para hoteles aplicado en riohacha 103 -- Experimentos con el sistema de recomendación hr.lsp 103 -- El modelo de sistema de recomendación para restaurantes 108 -- Identificación de criterios 109 -- Adquisición de datos 111 -- Modelización de preferencias en criterios uni-valuados 112 -- Modelización de preferencias para el criterio multivaluado food: puntuación de interés de la especialidad alimentaria- fsis 116 -- Arquitectura general del sistema de recomendación de restaurantes basado en lsp 118 -- Sistema de recomendación de restaurantes rr.lsp aplicado a riohacha 121 7) experimentación con diferentes perfiles de usuario del rr.lsp 124 -- Estudio comparativo 126 -- Validación con expertos 128 -- Análisis y discusión de algunos perfiles de usuarios 131 -- Discusión del estudio de perfiles 134 -- El sistema de recomendador web jimataa:implementación y evaluación de modelos 135 -- Proceso de funcionamiento de los sistemas de recomendación 144 -- Implantación y evaluación de sistemas de recomendación 146 -- Conclusiones del capítulo 153 -- Conclusiones y aportes 157 -- Conclusiones 157 -- Aportes 159 -- Trabajos futuros 161 -- Referencias 165Doctor(a) en Tecnologías de la Información y la ComunicaciónDoctorado187 páginasapplication/pdfspaCorporación Universidad de la CostaCiencias de la Computación y ElectrónicaBarranquilla, ColombiaDoctorado en Tecnologías de la Información y la ComunicaciónSistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turísticoTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesishttp://purl.org/redcol/resource_type/TDinfo:eu-repo/semantics/acceptedVersionJ. 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Softw., vol. 110, pp. 129–138, 2018, doi: 10.1016/j.envsoft.2018.04.003.Sistema de recomendaciónTurismoToma de decisiones multicriterioOperadores de agregaciónModelado de preferenciasLógica graduadaRecommender systemTourismMulti-criteria decision makingAggregation operatorsPreference modelingGraded logicPublicatione87ecb34-281c-4c57-8c50-41d1f22c3ff3virtual::1833-1e87ecb34-281c-4c57-8c50-41d1f22c3ff3virtual::1833-1https://scholar.google.com/citations?user=ioE32X8AAAAJ&hl=esvirtual::1833-10000-0002-1933-8496virtual::1833-1ORIGINALSistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino turístico.pdfSistema multicriterio de recomendación basado en operadores de lógica graduada de preferencias para la orientación segura en un destino 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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