Data-driven decision making in Critique-based recommenders: from a critique to social media data

In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ...

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Detalhes bibliográficos
Autores: Contreras, David, Salamó Llorente, Maria
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/195660
Acesso em linha:https://hdl.handle.net/2445/195660
Access Level:acceso abierto
Palavra-chave:Sistemes d'ajuda a la decisió
Algorismes computacionals
Mitjans socials
Decision support systems
Computer algorithms
Social media
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spelling Data-driven decision making in Critique-based recommenders: from a critique to social media dataContreras, DavidSalamó Llorente, MariaSistemes d'ajuda a la decisióAlgorismes computacionalsMitjans socialsDecision support systemsComputer algorithmsSocial mediaIn the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones.Springer Nature2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/195660Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/10.1007/s10844-018-0520-9Journal of Intelligent Information Systems, 2020, num. 54, p. 23-44https://doi.org/10.1007/s10844-018-0520-9(c) Springer Nature, 2020info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1956602026-05-27T06:46:51Z
dc.title.none.fl_str_mv Data-driven decision making in Critique-based recommenders: from a critique to social media data
title Data-driven decision making in Critique-based recommenders: from a critique to social media data
spellingShingle Data-driven decision making in Critique-based recommenders: from a critique to social media data
Contreras, David
Sistemes d'ajuda a la decisió
Algorismes computacionals
Mitjans socials
Decision support systems
Computer algorithms
Social media
title_short Data-driven decision making in Critique-based recommenders: from a critique to social media data
title_full Data-driven decision making in Critique-based recommenders: from a critique to social media data
title_fullStr Data-driven decision making in Critique-based recommenders: from a critique to social media data
title_full_unstemmed Data-driven decision making in Critique-based recommenders: from a critique to social media data
title_sort Data-driven decision making in Critique-based recommenders: from a critique to social media data
dc.creator.none.fl_str_mv Contreras, David
Salamó Llorente, Maria
author Contreras, David
author_facet Contreras, David
Salamó Llorente, Maria
author_role author
author2 Salamó Llorente, Maria
author2_role author
dc.subject.none.fl_str_mv Sistemes d'ajuda a la decisió
Algorismes computacionals
Mitjans socials
Decision support systems
Computer algorithms
Social media
topic Sistemes d'ajuda a la decisió
Algorismes computacionals
Mitjans socials
Decision support systems
Computer algorithms
Social media
description In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/195660
url https://hdl.handle.net/2445/195660
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1007/s10844-018-0520-9
Journal of Intelligent Information Systems, 2020, num. 54, p. 23-44
https://doi.org/10.1007/s10844-018-0520-9
dc.rights.none.fl_str_mv (c) Springer Nature, 2020
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Springer Nature, 2020
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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