A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at...

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Detalles Bibliográficos
Autores: Hernández-Rubio, María, Cantador Gutiérrez, Iván, Bellogin Kouki, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/692307
Acceso en línea:http://hdl.handle.net/10486/692307
https://dx.doi.org/10.1007/s11257-018-9214-9
Access Level:acceso abierto
Palabra clave:Recommender Systems
Aspect-based recommendation
Sentiment analysis
Opinion mining
Aspect extraction
User reviews
Informática
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spelling A comparative analysis of recommender systems based on item aspect opinions extracted from user reviewsHernández-Rubio, MaríaCantador Gutiérrez, IvánBellogin Kouki, AlejandroRecommender SystemsAspect-based recommendationSentiment analysisOpinion miningAspect extractionUser reviewsInformáticaThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11257-018-9214-9In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P).SpringerDepartamento de Ingeniería InformáticaEscuela Politécnica Superior20192019-04-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/692307https://dx.doi.org/10.1007/s11257-018-9214-9reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6923072026-06-23T12:46:27Z
dc.title.none.fl_str_mv A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
title A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
spellingShingle A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
Hernández-Rubio, María
Recommender Systems
Aspect-based recommendation
Sentiment analysis
Opinion mining
Aspect extraction
User reviews
Informática
title_short A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
title_full A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
title_fullStr A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
title_full_unstemmed A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
title_sort A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
dc.creator.none.fl_str_mv Hernández-Rubio, María
Cantador Gutiérrez, Iván
Bellogin Kouki, Alejandro
author Hernández-Rubio, María
author_facet Hernández-Rubio, María
Cantador Gutiérrez, Iván
Bellogin Kouki, Alejandro
author_role author
author2 Cantador Gutiérrez, Iván
Bellogin Kouki, Alejandro
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Recommender Systems
Aspect-based recommendation
Sentiment analysis
Opinion mining
Aspect extraction
User reviews
Informática
topic Recommender Systems
Aspect-based recommendation
Sentiment analysis
Opinion mining
Aspect extraction
User reviews
Informática
description This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11257-018-9214-9
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-04-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/692307
https://dx.doi.org/10.1007/s11257-018-9214-9
url http://hdl.handle.net/10486/692307
https://dx.doi.org/10.1007/s11257-018-9214-9
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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