Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems

Autores
Dalponte Ayastuy, María; Fernández, Alejandro; Torres, Diego
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.
Materia
Ciencias de la Computación e Información
Adaptive Gamification
Multi-criteria Recommender Systems
Clustering-based Collaborative Filtering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/12107
Acceder
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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/12107
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repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systemsDalponte Ayastuy, MaríaFernández, AlejandroTorres, DiegoCiencias de la Computación e InformaciónAdaptive GamificationMulti-criteria Recommender SystemsClustering-based Collaborative FilteringThis article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.2023info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12107enginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-35930-9_19info:eu-repo/semantics/altIdentifier/isbn/978-3-031-35930-9info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-10-23T11:14:16Zoai:digital.cic.gba.gob.ar:11746/12107Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-10-23 11:14:16.74CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
title Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
spellingShingle Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
Dalponte Ayastuy, María
Ciencias de la Computación e Información
Adaptive Gamification
Multi-criteria Recommender Systems
Clustering-based Collaborative Filtering
title_short Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
title_full Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
title_fullStr Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
title_full_unstemmed Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
title_sort Fits like a game: a multi-criteria adaptive gamification for collaborative location-based collecting systems
dc.creator.none.fl_str_mv Dalponte Ayastuy, María
Fernández, Alejandro
Torres, Diego
author Dalponte Ayastuy, María
author_facet Dalponte Ayastuy, María
Fernández, Alejandro
Torres, Diego
author_role author
author2 Fernández, Alejandro
Torres, Diego
author2_role author
author
dc.subject.none.fl_str_mv Ciencias de la Computación e Información
Adaptive Gamification
Multi-criteria Recommender Systems
Clustering-based Collaborative Filtering
topic Ciencias de la Computación e Información
Adaptive Gamification
Multi-criteria Recommender Systems
Clustering-based Collaborative Filtering
dc.description.none.fl_txt_mv This article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.
description This article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/12107
url https://digital.cic.gba.gob.ar/handle/11746/12107
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-35930-9_19
info:eu-repo/semantics/altIdentifier/isbn/978-3-031-35930-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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