Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants

Autores
Lopes, Alexandre; Bicharra Garcia, Ana Cristina
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Automated Collaborative Filtering (CF) techniques have been successfully applied on Recommendation domains. Dellarocas [1] proposes their use on reputation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [2]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior by the evaluated participants. It can turn evaluators with similar tastes into distant ones, contributing to poor reputation rates. A Reputation Model is proposed to minimize those problems. It uses CF techniques adjusted with the following improvements: 1) information of evaluators taste profiles is added to the user evaluation history; 2) transformations are applied on user evaluation history based on the similarities between the taste profiles of the active user and of the other evaluators to identify more reliable neighborhoods. An experiment is implemented through a simulated electronic marketplace where buyers choose sellers based on reputation estimates generated by the proposed reputation model and by a model that uses traditional CF. The goal is to compare the proposed model performance with the traditional one through comparative analysis of the data that is created. The results are explained at the end of the paper.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Filtering
Electronic Commerce
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23862

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spelling Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participantsLopes, AlexandreBicharra Garcia, Ana CristinaCiencias InformáticasFilteringElectronic CommerceAutomated Collaborative Filtering (CF) techniques have been successfully applied on Recommendation domains. Dellarocas [1] proposes their use on reputation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [2]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior by the evaluated participants. It can turn evaluators with similar tastes into distant ones, contributing to poor reputation rates. A Reputation Model is proposed to minimize those problems. It uses CF techniques adjusted with the following improvements: 1) information of evaluators taste profiles is added to the user evaluation history; 2) transformations are applied on user evaluation history based on the similarities between the taste profiles of the active user and of the other evaluators to identify more reliable neighborhoods. An experiment is implemented through a simulated electronic marketplace where buyers choose sellers based on reputation estimates generated by the proposed reputation model and by a model that uses traditional CF. The goal is to compare the proposed model performance with the traditional one through comparative analysis of the data that is created. The results are explained at the end of the paper.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23862enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:36Zoai:sedici.unlp.edu.ar:10915/23862Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:36.69SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
title Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
spellingShingle Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
Lopes, Alexandre
Ciencias Informáticas
Filtering
Electronic Commerce
title_short Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
title_full Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
title_fullStr Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
title_full_unstemmed Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
title_sort Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants
dc.creator.none.fl_str_mv Lopes, Alexandre
Bicharra Garcia, Ana Cristina
author Lopes, Alexandre
author_facet Lopes, Alexandre
Bicharra Garcia, Ana Cristina
author_role author
author2 Bicharra Garcia, Ana Cristina
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Filtering
Electronic Commerce
topic Ciencias Informáticas
Filtering
Electronic Commerce
dc.description.none.fl_txt_mv Automated Collaborative Filtering (CF) techniques have been successfully applied on Recommendation domains. Dellarocas [1] proposes their use on reputation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [2]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior by the evaluated participants. It can turn evaluators with similar tastes into distant ones, contributing to poor reputation rates. A Reputation Model is proposed to minimize those problems. It uses CF techniques adjusted with the following improvements: 1) information of evaluators taste profiles is added to the user evaluation history; 2) transformations are applied on user evaluation history based on the similarities between the taste profiles of the active user and of the other evaluators to identify more reliable neighborhoods. An experiment is implemented through a simulated electronic marketplace where buyers choose sellers based on reputation estimates generated by the proposed reputation model and by a model that uses traditional CF. The goal is to compare the proposed model performance with the traditional one through comparative analysis of the data that is created. The results are explained at the end of the paper.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1
Red de Universidades con Carreras en Informática (RedUNCI)
description Automated Collaborative Filtering (CF) techniques have been successfully applied on Recommendation domains. Dellarocas [1] proposes their use on reputation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [2]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior by the evaluated participants. It can turn evaluators with similar tastes into distant ones, contributing to poor reputation rates. A Reputation Model is proposed to minimize those problems. It uses CF techniques adjusted with the following improvements: 1) information of evaluators taste profiles is added to the user evaluation history; 2) transformations are applied on user evaluation history based on the similarities between the taste profiles of the active user and of the other evaluators to identify more reliable neighborhoods. An experiment is implemented through a simulated electronic marketplace where buyers choose sellers based on reputation estimates generated by the proposed reputation model and by a model that uses traditional CF. The goal is to compare the proposed model performance with the traditional one through comparative analysis of the data that is created. The results are explained at the end of the paper.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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