Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services

[EN]Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of m...

Descripción completa

Detalles Bibliográficos
Autores: Dang, Cach N., Moreno García, María Navelonga, Prieta Pintado, Fernando de la
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/166832
Acceso en línea:http://hdl.handle.net/10366/166832
Access Level:acceso abierto
Palabra clave:Sentiment analysis
Deep learning
Streaming services recommendation
Natural language processing
1203 Ciencia de los ordenadores
id ES_2d64d812ace6d3c3ebd575dc2bf9976f
oai_identifier_str oai:gredos.usal.es:10366/166832
network_acronym_str ES
network_name_str España
repository_id_str
spelling Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming ServicesDang, Cach N.Moreno García, María NavelongaPrieta Pintado, Fernando de laSentiment analysisDeep learningStreaming services recommendationNatural language processing1203 Ciencia de los ordenadores[EN]Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media data can be exploited to improve their reliability. In the case of media social data, sentiment analysis of the opinions expressed by users, together with properties of the items they consume, can help gain a better understanding of their preferences. In this study, we present a recommendation approach that integrates sentiment analysis and genre-based similarity in collaborative filtering methods. The proposal involves the use of BERT for genre preprocessing and feature extraction, as well as hybrid deep learning models, for sentiment analysis of user reviews. The approach was evaluated on popular public movie datasets. The experimental results show that the proposed approach significantly improves the recommender system performance.MDPI202520252021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10366/166832reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1668322026-06-07T06:28:51Z
dc.title.none.fl_str_mv Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
title Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
spellingShingle Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
Dang, Cach N.
Sentiment analysis
Deep learning
Streaming services recommendation
Natural language processing
1203 Ciencia de los ordenadores
title_short Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
title_full Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
title_fullStr Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
title_full_unstemmed Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
title_sort Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
dc.creator.none.fl_str_mv Dang, Cach N.
Moreno García, María Navelonga
Prieta Pintado, Fernando de la
author Dang, Cach N.
author_facet Dang, Cach N.
Moreno García, María Navelonga
Prieta Pintado, Fernando de la
author_role author
author2 Moreno García, María Navelonga
Prieta Pintado, Fernando de la
author2_role author
author
dc.subject.none.fl_str_mv Sentiment analysis
Deep learning
Streaming services recommendation
Natural language processing
1203 Ciencia de los ordenadores
topic Sentiment analysis
Deep learning
Streaming services recommendation
Natural language processing
1203 Ciencia de los ordenadores
description [EN]Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media data can be exploited to improve their reliability. In the case of media social data, sentiment analysis of the opinions expressed by users, together with properties of the items they consume, can help gain a better understanding of their preferences. In this study, we present a recommendation approach that integrates sentiment analysis and genre-based similarity in collaborative filtering methods. The proposal involves the use of BERT for genre preprocessing and feature extraction, as well as hybrid deep learning models, for sentiment analysis of user reviews. The approach was evaluated on popular public movie datasets. The experimental results show that the proposed approach significantly improves the recommender system performance.
publishDate 2021
dc.date.none.fl_str_mv 2021
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/166832
url http://hdl.handle.net/10366/166832
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405312413335552
score 15,81155

Similares en LA Referencia