RsRS: Ridesharing recommendation system based on social networks to improve the user’s QoE

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorLasmar, Eduardo L.-
Autor(es): dc.creatorPaula, Fábio O. de-
Autor(es): dc.creatorRosa, Renata L.-
Autor(es): dc.creatorAbrahão, Julia I.-
Autor(es): dc.creatorRodríguez, Demóstenes Z.-
Data de aceite: dc.date.accessioned2026-02-09T11:34:31Z-
Data de disponibilização: dc.date.available2026-02-09T11:34:31Z-
Data de envio: dc.date.issued2020-05-13-
Data de envio: dc.date.issued2020-05-13-
Data de envio: dc.date.issued2019-11-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/40870-
Fonte completa do material: dc.identifierhttps://ieeexplore.ieee.org/document/8865572-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1142697-
Descrição: dc.descriptionNowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users' quality is a tangible novel evaluation parameter. Consequently, this study treats the use of quality of experience (QoE) in the ridesharing service context, proposing a recommendation system (RS) for ridesharing services (RsRS), which considers user profile information extracted from online social networks (OSN) and user preferences. Thus, the main objective of the proposed RsRS is to improve users' QoE. To this end, the users' profile for the ridesharing service is built based on OSN data, which includes group of users with similar characteristics in the same trip, thus avoiding users with opposite preferences. First, subjective tests are carried out to obtain information on users' preferences and the results are analyzed via machine learning algorithms to determine the various user profiles. The experimental results demonstrate that the random forest algorithm has the best performance, considering OSN data and explicit preferences saved in the proposed solution and only OSN data, for average F-measures of 0.92 and 0.91, respectively. Additionally, a ranking containing a list of recommended users to share a ride is determined using a similarity function, and the results demonstrate that 94.2% of assessors agree with the proposed recommendations. Furthermore, the RsRS has a modular configuration and its integration with a real ridesharing service providers is also discussed.-
Idioma: dc.languageen-
Publicador: dc.publisherInstitute of Electrical and Electronic Engineers-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceIEEE Transactions on Intelligent Transportation Systems-
Palavras-chave: dc.subjectMachine learning algorithms-
Palavras-chave: dc.subjectPublic transportation-
Palavras-chave: dc.subjectSocial networking-
Palavras-chave: dc.subjectVehicles-
Palavras-chave: dc.subjectSmart phones-
Palavras-chave: dc.subjectReal-time systems-
Título: dc.titleRsRS: Ridesharing recommendation system based on social networks to improve the user’s QoE-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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